# Statistics Library Reference

Includes all of the Statistical Accumulators Library

`extractor`< `tag::count` > const
accumulator_base
std::size_t
void `dont_care`
result_type `dont_care`
dont_care
boost::accumulators::depends_on<>
tag::covariance< VariateType
VariateTag >
boost::accumulators::feature_of< tag::abstract_covariance >
tag::covariance< VariateType
VariateTag >
`tag::weighted_covariance`< VariateType, VariateTag >
tag::weighted_covariance< VariateType
VariateTag >
boost::accumulators::feature_of< tag::covariance< VariateType, VariateTag > >
`extractor`< `tag::abstract_covariance` > const
boost::accumulators::depends_on<>
void
functional::multiplies< Left, Right >
typename tag<Left>::type
typename tag<Right>::type
boost::numeric::functional::outer_product_base< Left, Right, void >
Left
Right
std_vector_tag
std_vector_tag
std::binary_function< Left, Right, ublas::matrix< functional::multiplies< Left::value_type, Right::value_type >::result_type > >
ublas::matrix< typename functional::multiplies< typename Left::value_type, typename Right::value_type >::result_type >
result_type `Left &` `Right &`
tag::density
`tag::weighted_density`
tag::weighted_density
boost::accumulators::feature_of< tag::density >
`extractor`< `tag::density` > const
boost::accumulators::depends_on< count, min, max >
density_cache_size
density_num_bins
boost::parameter::keyword< density_cache_size > const

tag::density::cache_size named parameter tag::density::num_bins named parameter

boost::parameter::keyword< density_num_bins > const
tag::error_of< Feature >
`tag::error_of`< typename `as_feature`< Feature >::type >
tag::error_of< Feature >
`tag::error_of`< typename `as_weighted_feature`< Feature >::type >
mean
boost::accumulators::depends_on< lazy_variance, count >
immediate_mean
boost::accumulators::depends_on< variance, count >
tag::extended_p_square
`tag::weighted_extended_p_square`
tag::weighted_extended_p_square
boost::accumulators::feature_of< tag::extended_p_square >
`extractor`< `tag::extended_p_square` > const
boost::accumulators::depends_on< count >
extended_p_square_probabilities
`accumulators::impl::extended_p_square_impl`< mpl::_1 >
boost::parameter::keyword< tag::probabilities > const tag::extended_p_square::probabilities named parameter
tag::extended_p_square_quantile(linear)
`tag::extended_p_square_quantile`
tag::extended_p_square_quantile(quadratic)
`tag::extended_p_square_quantile_quadratic`
tag::weighted_extended_p_square_quantile(linear)
`tag::weighted_extended_p_square_quantile`
tag::weighted_extended_p_square_quantile(quadratic)
`tag::weighted_extended_p_square_quantile_quadratic`
tag::extended_p_square_quantile
boost::accumulators::feature_of< tag::quantile >
tag::extended_p_square_quantile_quadratic
boost::accumulators::feature_of< tag::quantile >
tag::extended_p_square_quantile
`tag::weighted_extended_p_square_quantile`
tag::weighted_extended_p_square_quantile
boost::accumulators::feature_of< tag::extended_p_square_quantile >
tag::extended_p_square_quantile_quadratic
`tag::weighted_extended_p_square_quantile_quadratic`
tag::weighted_extended_p_square_quantile_quadratic
boost::accumulators::feature_of< tag::extended_p_square_quantile_quadratic >
`extractor`< `tag::extended_p_square_quantile` > const
`extractor`< `tag::extended_p_square_quantile_quadratic` > const
`extractor`< `tag::weighted_extended_p_square_quantile` > const
`extractor`< `tag::weighted_extended_p_square_quantile_quadratic` > const
boost::accumulators::depends_on< extended_p_square >
`accumulators::impl::extended_p_square_quantile_impl`< mpl::_1, `unweighted`, `linear` >
boost::accumulators::depends_on< extended_p_square >
`accumulators::impl::extended_p_square_quantile_impl`< mpl::_1, `unweighted`, `quadratic` >
boost::accumulators::depends_on< weighted_extended_p_square >
`accumulators::impl::extended_p_square_quantile_impl`< mpl::_1, `weighted`, `linear` >
boost::accumulators::depends_on< weighted_extended_p_square >
`accumulators::impl::extended_p_square_quantile_impl`< mpl::_1, `weighted`, `quadratic` >
tag::kurtosis
`tag::weighted_kurtosis`
tag::weighted_kurtosis
boost::accumulators::feature_of< tag::kurtosis >
`extractor`< `tag::kurtosis` > const
boost::accumulators::depends_on< mean, moment< 2 >, moment< 3 >, moment< 4 > >
`extractor`< `tag::max` > const
boost::accumulators::depends_on<>
tag::mean(lazy)
`tag::mean`
tag::mean(immediate)
`tag::immediate_mean`
tag::mean_of_weights(lazy)
`tag::mean_of_weights`
tag::mean_of_weights(immediate)
`tag::immediate_mean_of_weights`
tag::mean_of_variates< VariateType
VariateTag >(lazy)
`tag::mean_of_variates`< VariateType, VariateTag >
tag::mean_of_variates< VariateType
VariateTag >(immediate)
`tag::immediate_mean_of_variates`< VariateType, VariateTag >
tag::immediate_mean
boost::accumulators::feature_of< tag::mean >
tag::immediate_mean_of_weights
boost::accumulators::feature_of< tag::mean_of_weights >
tag::immediate_mean_of_variates< VariateType
VariateTag >
boost::accumulators::feature_of< tag::mean_of_variates< VariateType, VariateTag > >
tag::mean
`tag::weighted_mean`
tag::weighted_mean
boost::accumulators::feature_of< tag::mean >
tag::immediate_mean
`tag::immediate_weighted_mean`
tag::immediate_weighted_mean
boost::accumulators::feature_of< tag::immediate_mean >
tag::mean_of_variates< VariateType
VariateTag >
`tag::weighted_mean_of_variates`< VariateType, VariateTag >
tag::weighted_mean_of_variates< VariateType
VariateTag >
boost::accumulators::feature_of< tag::mean_of_variates< VariateType, VariateTag > >
tag::immediate_mean_of_variates< VariateType
VariateTag >
`tag::immediate_weighted_mean_of_variates`< VariateType, VariateTag >
tag::immediate_weighted_mean_of_variates< VariateType
VariateTag >
boost::accumulators::feature_of< tag::immediate_mean_of_variates< VariateType, VariateTag > >
`extractor`< `tag::mean` > const
`extractor`< `tag::mean_of_weights` > const
boost::accumulators::depends_on< count, sum >
boost::accumulators::depends_on< count >
boost::accumulators::depends_on< count, sum_of_weights >
mpl::true_
boost::accumulators::depends_on< count >
mpl::true_
tag::median(with_p_square_quantile)
`tag::median`
tag::median(with_density)
`tag::with_density_median`
tag::median(with_p_square_cumulative_distribution)
`tag::with_p_square_cumulative_distribution_median`
tag::with_density_median
boost::accumulators::feature_of< tag::median >
tag::with_p_square_cumulative_distribution_median
boost::accumulators::feature_of< tag::median >
tag::median
`tag::weighted_median`
tag::weighted_median
boost::accumulators::feature_of< tag::median >
tag::with_density_median
`tag::with_density_weighted_median`
tag::with_density_weighted_median
boost::accumulators::feature_of< tag::with_density_median >
tag::with_p_square_cumulative_distribution_median
`tag::with_p_square_cumulative_distribution_weighted_median`
tag::with_p_square_cumulative_distribution_weighted_median
boost::accumulators::feature_of< tag::with_p_square_cumulative_distribution_median >
`extractor`< `tag::median` > const
`extractor`< `tag::with_density_median` > const
`extractor`< `tag::with_p_square_cumulative_distribution_median` > const
boost::accumulators::depends_on< p_square_quantile_for_median >
boost::accumulators::depends_on< count, density >
boost::accumulators::depends_on< p_square_cumulative_distribution >
`extractor`< `tag::min` > const
boost::accumulators::depends_on<>
int
tag::moment< N >
`tag::weighted_moment`< N >
int
tag::weighted_moment< N >
boost::accumulators::feature_of< tag::moment< N > >
tag::p_square_cumulative_distribution
`tag::weighted_p_square_cumulative_distribution`
tag::weighted_p_square_cumulative_distribution
boost::accumulators::feature_of< tag::p_square_cumulative_distribution >
`extractor`< `tag::p_square_cumulative_distribution` > const
boost::accumulators::depends_on< count >
p_square_cumulative_distribution_num_cells
tag::p_square_quantile
`tag::weighted_p_square_quantile`
tag::weighted_p_square_quantile
boost::accumulators::feature_of< tag::p_square_quantile >
`extractor`< `tag::p_square_quantile` > const
`extractor`< `tag::p_square_quantile_for_median` > const
boost::accumulators::depends_on< count >
boost::accumulators::depends_on< count >
tag::peaks_over_threshold< LeftRight >(with_threshold_value)
`tag::peaks_over_threshold`< LeftRight >
tag::peaks_over_threshold< LeftRight >(with_threshold_probability)
`tag::peaks_over_threshold_prob`< LeftRight >
tag::peaks_over_threshold< LeftRight >
boost::accumulators::feature_of< tag::abstract_peaks_over_threshold >
tag::peaks_over_threshold_prob< LeftRight >
boost::accumulators::feature_of< tag::abstract_peaks_over_threshold >
tag::peaks_over_threshold< LeftRight >
`tag::weighted_peaks_over_threshold`< LeftRight >
tag::weighted_peaks_over_threshold< LeftRight >
boost::accumulators::feature_of< tag::peaks_over_threshold< LeftRight > >
tag::peaks_over_threshold_prob< LeftRight >
`tag::weighted_peaks_over_threshold_prob`< LeftRight >
tag::weighted_peaks_over_threshold_prob< LeftRight >
boost::accumulators::feature_of< tag::peaks_over_threshold_prob< LeftRight > >
`extractor`< `tag::abstract_peaks_over_threshold` > const
accumulator_base
Peaks over Threshold Method for Quantile and Tail Mean Estimation.

According to the theorem of Pickands-Balkema-de Haan, the distribution function $F_u(x)$$F_u(x)$ of the excesses $x$$x$ over some sufficiently high threshold $u$$u$ of a distribution function $F(x)$$F(x)$ may be approximated by a generalized Pareto distribution

#

\[ G_{\xi,\beta}(x) = \left\{ \begin{array}{ll} \beta^{-1}\left(1+\frac{\xi x}{\beta}\right)^{-1/\xi-1} & \textrm{if }\xi\neq0\\ \beta^{-1}\exp\left(-\frac{x}{\beta}\right) & \textrm{if }\xi=0, \end{array} \right. \]\[ G_{\xi,\beta}(x) = \left\{ \begin{array}{ll} \beta^{-1}\left(1+\frac{\xi x}{\beta}\right)^{-1/\xi-1} & \textrm{if }\xi\neq0\\ \beta^{-1}\exp\left(-\frac{x}{\beta}\right) & \textrm{if }\xi=0, \end{array} \right. \] with suitable parameters $\xi$$\xi$ and $\beta$$\beta$ that can be estimated, e.g., with the method of moments, cf. Hosking and Wallis (1987),

#

\[ \begin{array}{lll} \hat{\xi} & = & \frac{1}{2}\left[1-\frac{(\hat{\mu}-u)^2}{\hat{\sigma}^2}\right]\\ \hat{\beta} & = & \frac{\hat{\mu}-u}{2}\left[\frac{(\hat{\mu}-u)^2}{\hat{\sigma}^2}+1\right], \end{array} \]\[ \begin{array}{lll} \hat{\xi} & = & \frac{1}{2}\left[1-\frac{(\hat{\mu}-u)^2}{\hat{\sigma}^2}\right]\\ \hat{\beta} & = & \frac{\hat{\mu}-u}{2}\left[\frac{(\hat{\mu}-u)^2}{\hat{\sigma}^2}+1\right], \end{array} \] $\hat{\mu}$$\hat{\mu}$ and $\hat{\sigma}^2$$\hat{\sigma}^2$ being the empirical mean and variance of the samples over the threshold $u$$u$. Equivalently, the distribution function $F_u(x-u)$$F_u(x-u)$ of the exceedances $x-u$$x-u$ can be approximated by $G_{\xi,\beta}(x-u)=G_{\xi,\beta,u}(x)$$G_{\xi,\beta}(x-u)=G_{\xi,\beta,u}(x)$. Since for $x\geq u$$x\geq u$ the distribution function $F(x)$$F(x)$ can be written as

#

\[ F(x) = [1 - \P(X \leq u)]F_u(x - u) + \P(X \leq u) \]\[ F(x) = [1 - \P(X \leq u)]F_u(x - u) + \P(X \leq u) \] and the probability $\P(X \leq u)$$\P(X \leq u)$ can be approximated by the empirical distribution function $F_n(u)$$F_n(u)$ evaluated at $u$$u$, an estimator of $F(x)$$F(x)$ is given by

#

\[ \widehat{F}(x) = [1 - F_n(u)]G_{\xi,\beta,u}(x) + F_n(u). \]\[ \widehat{F}(x) = [1 - F_n(u)]G_{\xi,\beta,u}(x) + F_n(u). \] It can be shown that $\widehat{F}(x)$$\widehat{F}(x)$ is a generalized Pareto distribution $G_{\xi,\bar{\beta},\bar{u}}(x)$$G_{\xi,\bar{\beta},\bar{u}}(x)$ with $\bar{\beta}=\beta[1-F_n(u)]^{\xi}$$\bar{\beta}=\beta[1-F_n(u)]^{\xi}$ and $\bar{u}=u-\bar{\beta}\left\{[1-F_n(u)]^{-\xi}-1\right\}/\xi$$\bar{u}=u-\bar{\beta}\left\{[1-F_n(u)]^{-\xi}-1\right\}/\xi$. By inverting $\widehat{F}(x)$$\widehat{F}(x)$, one obtains an estimator for the $\alpha$$\alpha$-quantile,

#

\[ \hat{q}_{\alpha} = \bar{u} + \frac{\bar{\beta}}{\xi}\left[(1-\alpha)^{-\xi}-1\right], \]\[ \hat{q}_{\alpha} = \bar{u} + \frac{\bar{\beta}}{\xi}\left[(1-\alpha)^{-\xi}-1\right], \] and similarly an estimator for the (coherent) tail mean,

#

\[ \widehat{CTM}_{\alpha} = \hat{q}_{\alpha} - \frac{\bar{\beta}}{\xi-1}(1-\alpha)^{-\xi}, \]\[ \widehat{CTM}_{\alpha} = \hat{q}_{\alpha} - \frac{\bar{\beta}}{\xi-1}(1-\alpha)^{-\xi}, \] cf. McNeil and Frey (2000).

Note that in case extreme values of the left tail are fitted, the distribution is mirrored with respect to the $y$$y$ axis such that the left tail can be treated as a right tail. The computed fit parameters thus define the Pareto distribution that fits the mirrored left tail. When quantities like a quantile or a tail mean are computed using the fit parameters obtained from the mirrored data, the result is mirrored back, yielding the correct result.

For further details, see

J. R. M. Hosking and J. R. Wallis, Parameter and quantile estimation for the generalized Pareto distribution, Technometrics, Volume 29, 1987, p. 339-349

A. J. McNeil and R. Frey, Estimation of Tail-Related Risk Measures for Heteroscedastic Financial Time Series: an Extreme Value Approach, Journal of Empirical Finance, Volume 7, 2000, p. 271-300

numeric::functional::fdiv< Sample, std::size_t >::result_type
boost::tuple< float_type, float_type, float_type >
mpl::int_< is_same< LeftRight, `left` >::value?-1:1 >
void `Args const &`
result_type `Args const &`
Args const &
boost::accumulators::depends_on<>
tag::pot_quantile< LeftRight >(with_threshold_value)
`tag::pot_quantile`< LeftRight >
tag::pot_quantile< LeftRight >(with_threshold_probability)
`tag::pot_quantile_prob`< LeftRight >
tag::weighted_pot_quantile< LeftRight >(with_threshold_value)
`tag::weighted_pot_quantile`< LeftRight >
tag::weighted_pot_quantile< LeftRight >(with_threshold_probability)
`tag::weighted_pot_quantile_prob`< LeftRight >
tag::pot_quantile< LeftRight >
boost::accumulators::feature_of< tag::quantile >
tag::pot_quantile_prob< LeftRight >
boost::accumulators::feature_of< tag::quantile >
tag::pot_quantile< LeftRight >
`tag::weighted_pot_quantile`< LeftRight >
tag::weighted_pot_quantile< LeftRight >
boost::accumulators::feature_of< tag::pot_quantile< LeftRight > >
tag::pot_quantile_prob< LeftRight >
`tag::weighted_pot_quantile_prob`< LeftRight >
tag::weighted_pot_quantile_prob< LeftRight >
boost::accumulators::feature_of< tag::pot_quantile_prob< LeftRight > >
tag::pot_tail_mean< LeftRight >(with_threshold_value)
`tag::pot_tail_mean`< LeftRight >
tag::pot_tail_mean< LeftRight >(with_threshold_probability)
`tag::pot_tail_mean_prob`< LeftRight >
tag::weighted_pot_tail_mean< LeftRight >(with_threshold_value)
`tag::weighted_pot_tail_mean`< LeftRight >
tag::weighted_pot_tail_mean< LeftRight >(with_threshold_probability)
`tag::weighted_pot_tail_mean_prob`< LeftRight >
tag::pot_tail_mean< LeftRight >
boost::accumulators::feature_of< tag::tail_mean >
tag::pot_tail_mean_prob< LeftRight >
boost::accumulators::feature_of< tag::tail_mean >
tag::pot_tail_mean< LeftRight >
`tag::weighted_pot_tail_mean`< LeftRight >
tag::weighted_pot_tail_mean< LeftRight >
boost::accumulators::feature_of< tag::pot_tail_mean< LeftRight > >
tag::pot_tail_mean_prob< LeftRight >
`tag::weighted_pot_tail_mean_prob`< LeftRight >
tag::weighted_pot_tail_mean_prob< LeftRight >
boost::accumulators::feature_of< tag::pot_tail_mean_prob< LeftRight > >
`extractor`< `tag::rolling_count` > const
boost::accumulators::depends_on< rolling_window_plus1 >
boost::parameter::keyword< tag::rolling_window_size > const tag::rolling_window::window_size named parameter
tag::rolling_mean(lazy)
`tag::lazy_rolling_mean`
tag::rolling_mean(immediate)
`tag::immediate_rolling_mean`
tag::immediate_rolling_mean
boost::accumulators::feature_of< tag::rolling_mean >
tag::lazy_rolling_mean
boost::accumulators::feature_of< tag::rolling_mean >
`extractor`< `tag::lazy_rolling_mean` > const
`extractor`< `tag::immediate_rolling_mean` > const
`extractor`< `tag::rolling_mean` > const
accumulator_base
numeric::functional::fdiv< Sample, std::size_t, void, void >::result_type
result_type `Args const &`
dont_care
accumulator_base
numeric::functional::fdiv< Sample, std::size_t >::result_type
void `Args const &`
result_type `Args const &`
Args const &
boost::accumulators::depends_on< rolling_sum, rolling_count >
boost::parameter::keyword< tag::rolling_window_size > const tag::rolling_window::window_size named parameter
boost::accumulators::depends_on< rolling_window_plus1, rolling_count >
boost::parameter::keyword< tag::rolling_window_size > const tag::rolling_window::window_size named parameter
boost::accumulators::tag::immediate_rolling_mean
accumulator_base
numeric::functional::fdiv< Sample, std::size_t, void, void >::result_type
N::value
0
void `Args const &`
result_type `Args const &`
Args const &
int
boost::accumulators::depends_on< rolling_window_plus1, rolling_count >
boost::parameter::keyword< tag::rolling_window_size > const tag::rolling_window::window_size named parameter
`extractor`< `tag::rolling_sum` > const
boost::accumulators::depends_on< rolling_window_plus1 >
boost::parameter::keyword< tag::rolling_window_size > const tag::rolling_window::window_size named parameter
tag::rolling_variance(lazy)
`tag::lazy_rolling_variance`
tag::rolling_variance(immediate)
`tag::immediate_rolling_variance`
tag::lazy_rolling_variance
boost::accumulators::feature_of< tag::rolling_variance >
tag::immediate_rolling_variance
boost::accumulators::feature_of< tag::rolling_variance >
`extractor`< `tag::lazy_rolling_variance` > const
`extractor`< `tag::immediate_rolling_variance` > const
`extractor`< `tag::rolling_variance` > const
accumulator_base
Immediate (lazy) calculation of the rolling variance.

Calculation of sample variance $\sigma_n^2$$\sigma_n^2$ is done as follows, see also http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance. For a rolling window of size $N$$N$, when $n <= N$$n <= N$, the variance is computed according to the formula

#

\[ \sigma_n^2 = \frac{1}{n-1} \sum_{i = 1}^n (x_i - \mu_n)^2. \]\[ \sigma_n^2 = \frac{1}{n-1} \sum_{i = 1}^n (x_i - \mu_n)^2. \] When $n > N$$n > N$, the sample variance over the window becomes:

#

\[ \sigma_n^2 = \frac{1}{N-1} \sum_{i = n-N+1}^n (x_i - \mu_n)^2. \]\[ \sigma_n^2 = \frac{1}{N-1} \sum_{i = n-N+1}^n (x_i - \mu_n)^2. \]
numeric::functional::fdiv< Sample, std::size_t, void, void >::result_type
result_type `Args const &`
dont_care
accumulator_base
Iterative calculation of the rolling variance.

Iterative calculation of sample variance $\sigma_n^2$$\sigma_n^2$ is done as follows, see also http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance. For a rolling window of size $N$$N$, for the first $N$$N$ samples, the variance is computed according to the formula

#

\[ \sigma_n^2 = \frac{1}{n-1} \sum_{i = 1}^n (x_i - \mu_n)^2 = \frac{1}{n-1}M_{2,n}, \]\[ \sigma_n^2 = \frac{1}{n-1} \sum_{i = 1}^n (x_i - \mu_n)^2 = \frac{1}{n-1}M_{2,n}, \] where the sum of squares $M_{2,n}$$M_{2,n}$ can be recursively computed as:

#

\[ M_{2,n} = \sum_{i = 1}^n (x_i - \mu_n)^2 = M_{2,n-1} + (x_n - \mu_n)(x_n - \mu_{n-1}), \]\[ M_{2,n} = \sum_{i = 1}^n (x_i - \mu_n)^2 = M_{2,n-1} + (x_n - \mu_n)(x_n - \mu_{n-1}), \] and the estimate of the sample mean as:

#

\[ \mu_n = \frac{1}{n} \sum_{i = 1}^n x_i = \mu_{n-1} + \frac{1}{n}(x_n - \mu_{n-1}). \]\[ \mu_n = \frac{1}{n} \sum_{i = 1}^n x_i = \mu_{n-1} + \frac{1}{n}(x_n - \mu_{n-1}). \] For further samples, when the rolling window is fully filled with data, one has to take into account that the oldest sample $x_{n-N}$$x_{n-N}$ is dropped from the window. The sample variance over the window now becomes:

#

\[ \sigma_n^2 = \frac{1}{N-1} \sum_{i = n-N+1}^n (x_i - \mu_n)^2 = \frac{1}{n-1}M_{2,n}, \]\[ \sigma_n^2 = \frac{1}{N-1} \sum_{i = n-N+1}^n (x_i - \mu_n)^2 = \frac{1}{n-1}M_{2,n}, \] where the sum of squares $M_{2,n}$$M_{2,n}$ now equals:

#

\[ M_{2,n} = \sum_{i = n-N+1}^n (x_i - \mu_n)^2 = M_{2,n-1} + (x_n - \mu_n)(x_n - \mu_{n-1}) - (x_{n-N} - \mu_n)(x_{n-N} - \mu_{n-1}), \]\[ M_{2,n} = \sum_{i = n-N+1}^n (x_i - \mu_n)^2 = M_{2,n-1} + (x_n - \mu_n)(x_n - \mu_{n-1}) - (x_{n-N} - \mu_n)(x_{n-N} - \mu_{n-1}), \] and the estimated mean is:

#

\[ \mu_n = \frac{1}{N} \sum_{i = n-N+1}^n x_i = \mu_{n-1} + \frac{1}{n}(x_n - x_{n-N}). \]\[ \mu_n = \frac{1}{N} \sum_{i = n-N+1}^n x_i = \mu_{n-1} + \frac{1}{n}(x_n - x_{n-N}). \]

Note that the sample variance is not defined for $n <= 1$$n <= 1$.

numeric::functional::fdiv< Sample, std::size_t >::result_type
void `Args const &`
result_type `Args const &`
Args const &
void `T &` `typename boost::enable_if< boost::is_arithmetic< T >, T >::type *0`
void `T &` `typename boost::disable_if< boost::is_arithmetic< T >, T >::type *0`
boost::accumulators::depends_on< rolling_count, rolling_mean, rolling_moment< 2 > >
boost::parameter::keyword< tag::rolling_window_size > const tag::rolling_window::window_size named parameter
boost::accumulators::depends_on< rolling_window_plus1, rolling_count, immediate_rolling_mean >
boost::parameter::keyword< tag::rolling_window_size > const tag::rolling_window::window_size named parameter
boost::accumulators::tag::immediate_rolling_variance
`extractor`< `tag::rolling_window_plus1` > const
`extractor`< `tag::rolling_window` > const
bool `Args const &`
boost::accumulators::depends_on<>
rolling_window_size
boost::parameter::keyword< tag::rolling_window_size > const tag::rolling_window::size named parameter
boost::accumulators::depends_on< rolling_window_plus1 >
boost::parameter::keyword< tag::rolling_window_size > const tag::rolling_window::size named parameter
tag::skewness
`tag::weighted_skewness`
tag::weighted_skewness
boost::accumulators::feature_of< tag::skewness >
`extractor`< `tag::skewness` > const
boost::accumulators::depends_on< mean, moment< 2 >, moment< 3 > >

Contains the stats<> template.

tag::sum
`tag::weighted_sum`
tag::weighted_sum
boost::accumulators::feature_of< tag::sum >
tag::sum_of_variates< VariateType
VariateTag >
boost::accumulators::feature_of< tag::abstract_sum_of_variates >
`extractor`< `tag::sum` > const
`extractor`< `tag::sum_of_weights` > const
`extractor`< `tag::abstract_sum_of_variates` > const
boost::accumulators::depends_on<>
boost::accumulators::depends_on<>
mpl::true_
boost::accumulators::depends_on<>
tag::sum(kahan)
`tag::sum_kahan`
tag::sum_of_weights(kahan)
`tag::sum_of_weights_kahan`
tag::sum_kahan
`tag::weighted_sum_kahan`
tag::weighted_sum_kahan
boost::accumulators::feature_of< tag::sum >
tag::sum_kahan
boost::accumulators::feature_of< tag::sum >
tag::sum_of_weights_kahan
boost::accumulators::feature_of< tag::sum_of_weights >
tag::sum_of_variates_kahan< VariateType
VariateTag >
boost::accumulators::feature_of< tag::abstract_sum_of_variates >
`extractor`< `tag::sum_kahan` > const
`extractor`< `tag::sum_of_weights_kahan` > const
`extractor`< `tag::abstract_sum_of_variates` > const
boost::accumulators::depends_on<>
boost::accumulators::depends_on<>
mpl::true_
left
left_tail_cache_size
right
right_tail_cache_size
tag::tail< LeftRight >
boost::accumulators::feature_of< tag::abstract_tail >
`extractor`< `tag::abstract_tail` > const
boost::accumulators::depends_on<>
tag::coherent_tail_mean< LeftRight >
boost::accumulators::feature_of< tag::tail_mean >
tag::non_coherent_tail_mean< LeftRight >
boost::accumulators::feature_of< tag::abstract_non_coherent_tail_mean >
tag::non_coherent_tail_mean< LeftRight >
`tag::non_coherent_weighted_tail_mean`< LeftRight >
tag::non_coherent_weighted_tail_mean< LeftRight >
boost::accumulators::feature_of< tag::non_coherent_tail_mean< LeftRight > >
`extractor`< `tag::abstract_non_coherent_tail_mean` > const
`extractor`< `tag::tail_mean` > const
boost::accumulators::depends_on<>
tag::tail_quantile< LeftRight >
boost::accumulators::feature_of< tag::quantile >
tag::tail_quantile< LeftRight >
`tag::weighted_tail_quantile`< LeftRight >
tag::weighted_tail_quantile< LeftRight >
boost::accumulators::feature_of< tag::tail_quantile< LeftRight > >
`extractor`< `tag::quantile` > const
tag::tail_variate< VariateType
VariateTag
LeftRight >
boost::accumulators::feature_of< tag::abstract_tail_variate >
tag::tail_weights< LeftRight >
`tag::abstract_tail_weights`
`extractor`< `tag::abstract_tail_variate` > const
`extractor`< `tag::abstract_tail_weights` > const
boost::accumulators::depends_on<>
boost::accumulators::depends_on<>
tag::tail_variate_means< LeftRight
VariateType
VariateTag >(absolute)
`tag::absolute_tail_variate_means`< LeftRight, VariateType, VariateTag >
tag::tail_variate_means< LeftRight
VariateType
VariateTag >(relative)
`tag::relative_tail_variate_means`< LeftRight, VariateType, VariateTag >
tag::absolute_tail_variate_means< LeftRight
VariateType
VariateTag >
boost::accumulators::feature_of< tag::abstract_absolute_tail_variate_means >
tag::relative_tail_variate_means< LeftRight
VariateType
VariateTag >
boost::accumulators::feature_of< tag::abstract_relative_tail_variate_means >
tag::absolute_tail_variate_means< LeftRight
VariateType
VariateTag >
`tag::absolute_weighted_tail_variate_means`< LeftRight, VariateType, VariateTag >
tag::absolute_weighted_tail_variate_means< LeftRight
VariateType
VariateTag >
boost::accumulators::feature_of< tag::absolute_tail_variate_means< LeftRight, VariateType, VariateTag > >
tag::relative_tail_variate_means< LeftRight
VariateType
VariateTag >
`tag::relative_weighted_tail_variate_means`< LeftRight, VariateType, VariateTag >
tag::relative_weighted_tail_variate_means< LeftRight
VariateType
VariateTag >
boost::accumulators::feature_of< tag::relative_tail_variate_means< LeftRight, VariateType, VariateTag > >
`extractor`< `tag::abstract_absolute_tail_variate_means` > const
`extractor`< `tag::abstract_relative_tail_variate_means` > const
boost::accumulators::depends_on<>
boost::accumulators::depends_on<>
tag::variance(lazy)
`tag::lazy_variance`
tag::variance(immediate)
`tag::variance`
tag::lazy_variance
boost::accumulators::feature_of< tag::variance >
tag::variance
`tag::weighted_variance`
tag::weighted_variance
boost::accumulators::feature_of< tag::variance >
tag::lazy_variance
`tag::lazy_weighted_variance`
tag::lazy_weighted_variance
boost::accumulators::feature_of< tag::lazy_variance >
`extractor`< `tag::lazy_variance` > const
`extractor`< `tag::variance` > const
boost::accumulators::depends_on< moment< 2 >, mean >
boost::accumulators::depends_on< count, immediate_mean >
boost::parameter::keyword< `tag::covariate1` > const
boost::parameter::keyword< `tag::covariate2` > const
`extractor`< `tag::abstract_covariance` > const
`extractor`< `tag::density` > const
boost::accumulators::depends_on< count, sum_of_weights, min, max >
density_cache_size
density_num_bins
boost::parameter::keyword< density_cache_size > const
boost::parameter::keyword< density_num_bins > const
`extractor`< `tag::weighted_extended_p_square` > const
boost::accumulators::depends_on< count, sum_of_weights >
extended_p_square_probabilities
`accumulators::impl::weighted_extended_p_square_impl`< mpl::_1, mpl::_2 >
`extractor`< `tag::weighted_kurtosis` > const
boost::accumulators::depends_on< weighted_mean, weighted_moment< 2 >, weighted_moment< 3 >, weighted_moment< 4 > >
tag::weighted_mean(lazy)
`tag::weighted_mean`
tag::weighted_mean(immediate)
`tag::immediate_weighted_mean`
tag::weighted_mean_of_variates< VariateType
VariateTag >(lazy)
`tag::weighted_mean_of_variates`< VariateType, VariateTag >
tag::weighted_mean_of_variates< VariateType
VariateTag >(immediate)
`tag::immediate_weighted_mean_of_variates`< VariateType, VariateTag >
`extractor`< `tag::mean` > const
boost::accumulators::depends_on< sum_of_weights, weighted_sum >
boost::accumulators::depends_on< sum_of_weights >
tag::weighted_median(with_p_square_quantile)
`tag::weighted_median`
tag::weighted_median(with_density)
`tag::with_density_weighted_median`
tag::weighted_median(with_p_square_cumulative_distribution)
`tag::with_p_square_cumulative_distribution_weighted_median`
`extractor`< `tag::median` > const
boost::accumulators::depends_on< weighted_p_square_quantile_for_median >
boost::accumulators::depends_on< count, weighted_density >
boost::accumulators::depends_on< weighted_p_square_cumulative_distribution >
`extractor`< `tag::weighted_p_square_cumulative_distribution` > const
boost::accumulators::depends_on< count, sum_of_weights >
p_square_cumulative_distribution_num_cells
`accumulators::impl::weighted_p_square_cumulative_distribution_impl`< mpl::_1, mpl::_2 >
`extractor`< `tag::weighted_p_square_quantile` > const
`extractor`< `tag::weighted_p_square_quantile_for_median` > const
boost::accumulators::depends_on< count, sum_of_weights >
`accumulators::impl::weighted_p_square_quantile_impl`< mpl::_1, mpl::_2, `regular` >
boost::accumulators::depends_on< count, sum_of_weights >
`accumulators::impl::weighted_p_square_quantile_impl`< mpl::_1, mpl::_2, `for_median` >
tag::weighted_peaks_over_threshold< LeftRight >(with_threshold_value)
`tag::weighted_peaks_over_threshold`< LeftRight >
tag::weighted_peaks_over_threshold< LeftRight >(with_threshold_probability)
`tag::weighted_peaks_over_threshold_prob`< LeftRight >
`extractor`< `tag::abstract_peaks_over_threshold` > const
`extractor`< `tag::weighted_skewness` > const
boost::accumulators::depends_on< weighted_mean, weighted_moment< 2 >, weighted_moment< 3 > >
tag::weighted_sum_of_variates< VariateType
VariateTag >
boost::accumulators::feature_of< tag::abstract_weighted_sum_of_variates >
`extractor`< `tag::weighted_sum` > const
`extractor`< `tag::abstract_weighted_sum_of_variates` > const
boost::accumulators::depends_on<>
boost::accumulators::depends_on<>
tag::weighted_sum(kahan)
`tag::weighted_sum_kahan`
tag::weighted_sum_of_variates_kahan< VariateType
VariateTag >
boost::accumulators::feature_of< tag::abstract_weighted_sum_of_variates >
`extractor`< `tag::weighted_sum_kahan` > const
`extractor`< `tag::abstract_weighted_sum_of_variates` > const
boost::accumulators::depends_on<>
boost::accumulators::depends_on<>
`extractor`< `tag::abstract_non_coherent_tail_mean` > const
`extractor`< `tag::quantile` > const
tag::weighted_tail_variate_means< LeftRight
VariateType
VariateTag >(absolute)
`tag::absolute_weighted_tail_variate_means`< LeftRight, VariateType, VariateTag >
tag::weighted_tail_variate_means< LeftRight
VariateType
VariateTag >(relative)
`tag::relative_weighted_tail_variate_means`< LeftRight, VariateType, VariateTag >
`extractor`< `tag::abstract_absolute_tail_variate_means` > const
`extractor`< `tag::abstract_relative_tail_variate_means` > const
functional::multiplies< T, double const >
tag::weighted_variance(lazy)
`tag::lazy_weighted_variance`
tag::weighted_variance(immediate)
`tag::weighted_variance`
`extractor`< `tag::lazy_weighted_variance` > const
`extractor`< `tag::weighted_variance` > const
boost::accumulators::depends_on< weighted_moment< 2 >, weighted_mean >
boost::accumulators::depends_on< count, immediate_weighted_mean >
BOOST_PP_ENUM_PARAMS_WITH_A_DEFAULT(BOOST_ACCUMULATORS_MAX_FEATURES, typename Feature, mpl::na)
mpl::vector< Stat1, Stat2,...>
An MPL sequence of statistics.
BOOST_PP_ENUM_PARAMS_WITH_A_DEFAULT(BOOST_ACCUMULATORS_MAX_FEATURES, typename Feature, mpl::na)
`extractor`< `tag::quantile` > const
`extractor`< `tag::tail_mean` > const
accumulator_base
Covariance Estimator.

An iterative Monte Carlo estimator for the covariance $\mathrm{Cov}(X,X')$$\mathrm{Cov}(X,X')$, where $X$$X$ is a sample and $X'$$X'$ is a variate, is given by:

#

\[ \hat{c}_n = \frac{n-1}{n} \hat{c}_{n-1} + \frac{1}{n-1}(X_n - \hat{\mu}_n)(X_n' - \hat{\mu}_n'),\quad n\ge2,\quad\hat{c}_1 = 0, \]\[ \hat{c}_n = \frac{n-1}{n} \hat{c}_{n-1} + \frac{1}{n-1}(X_n - \hat{\mu}_n)(X_n' - \hat{\mu}_n'),\quad n\ge2,\quad\hat{c}_1 = 0, \]

$\hat{\mu}_n$$\hat{\mu}_n$ and $\hat{\mu}_n'$$\hat{\mu}_n'$ being the means of the samples and variates.

void `Args const &`
result_type `dont_care`
Args const &
accumulator_base
Histogram density estimator.

The histogram density estimator returns a histogram of the sample distribution. The positions and sizes of the bins are determined using a specifiable number of cached samples (cache_size). The range between the minimum and the maximum of the cached samples is subdivided into a specifiable number of bins (num_bins) of same size. Additionally, an under- and an overflow bin is added to capture future under- and overflow samples. Once the bins are determined, the cached samples and all subsequent samples are added to the correct bins. At the end, a range of std::pair is return, where each pair contains the position of the bin (lower bound) and the samples count (normalized with the total number of samples).

void `Args const &`
result_type `Args const &`

The number of samples must meet or exceed the cache size

Args const &
accumulator_base
result_type `Args const &`
dont_care
accumulator_base
Multiple quantile estimation with the extended $P^2$$P^2$ algorithm.

Extended $P^2$$P^2$ algorithm for estimation of several quantiles without storing samples. Assume that $m$$m$ quantiles $\xi_{p_1}, \ldots, \xi_{p_m}$$\xi_{p_1}, \ldots, \xi_{p_m}$ are to be estimated. Instead of storing the whole sample cumulative distribution, the algorithm maintains only $m+2$$m+2$ principal markers and $m+1$$m+1$ middle markers, whose positions are updated with each sample and whose heights are adjusted (if necessary) using a piecewise-parablic formula. The heights of these central markers are the current estimates of the quantiles and returned as an iterator range.

For further details, see

K. E. E. Raatikainen, Simultaneous estimation of several quantiles, Simulation, Volume 49, Number 4 (October), 1986, p. 159-164.

The extended $ P^2 $$ P^2 $ algorithm generalizes the $ P^2 $$ P^2 $ algorithm of

R. Jain and I. Chlamtac, The P^2 algorithm for dynamic calculation of quantiles and histograms without storing observations, Communications of the ACM, Volume 28 (October), Number 10, 1985, p. 1076-1085.

void `Args const &`
result_type `dont_care`
Args const &
accumulator_base
Quantile estimation using the extended $P^2$$P^2$ algorithm for weighted and unweighted samples.

Uses the quantile estimates calculated by the extended $P^2$$P^2$ algorithm to compute intermediate quantile estimates by means of quadratic interpolation.

result_type `Args const &`
Args const &
accumulator_base
Kurtosis estimation.

The kurtosis of a sample distribution is defined as the ratio of the 4th central moment and the square of the 2nd central moment (the variance) of the samples, minus 3. The term $ -3 $$ -3 $ is added in order to ensure that the normal distribution has zero kurtosis. The kurtosis can also be expressed by the simple moments:

#

\[ \hat{g}_2 = \frac {\widehat{m}_n^{(4)}-4\widehat{m}_n^{(3)}\hat{\mu}_n+6\widehat{m}_n^{(2)}\hat{\mu}_n^2-3\hat{\mu}_n^4} {\left(\widehat{m}_n^{(2)} - \hat{\mu}_n^{2}\right)^2} - 3, \]\[ \hat{g}_2 = \frac {\widehat{m}_n^{(4)}-4\widehat{m}_n^{(3)}\hat{\mu}_n+6\widehat{m}_n^{(2)}\hat{\mu}_n^2-3\hat{\mu}_n^4} {\left(\widehat{m}_n^{(2)} - \hat{\mu}_n^{2}\right)^2} - 3, \]

where $ \widehat{m}_n^{(i)} $$ \widehat{m}_n^{(i)} $ are the $ i $$ i $-th moment and $ \hat{\mu}_n $$ \hat{\mu}_n $ the mean (first moment) of the $ n $$ n $ samples.

result_type `Args const &`
dont_care
accumulator_base
void `Args const &`
result_type `dont_care`
Args const &
accumulator_base
Median estimation based on the $P^2$$P^2$ quantile estimator.

The $P^2$$P^2$ algorithm is invoked with a quantile probability of 0.5.

result_type `Args const &`
dont_care
accumulator_base
Median estimation based on the density estimator.

The algorithm determines the bin in which the $0.5*cnt$$0.5*cnt$-th sample lies, $cnt$$cnt$ being the total number of samples. It returns the approximate horizontal position of this sample, based on a linear interpolation inside the bin.

void `dont_care`
result_type `Args const &`
Args const &
accumulator_base
Median estimation based on the $P^2$$P^2$ cumulative distribution estimator.

The algorithm determines the first (leftmost) bin with a height exceeding 0.5. It returns the approximate horizontal position of where the cumulative distribution equals 0.5, based on a linear interpolation inside the bin.

void `dont_care`
result_type `Args const &`
dont_care
accumulator_base
void `Args const &`
result_type `dont_care`
Args const &

boost::accumulators::tag::sum

tag::sum
accumulator_base
result_type `Args const &`
dont_care
tag::sample
accumulator_base
void `Args const &`
result_type `dont_care`
Args const &
accumulator_base
N::value
0
void `Args const &`
result_type `Args const &`
Args const &
accumulator_base
Peaks over Threshold Method for Quantile and Tail Mean Estimation.

See Also:

`peaks_over_threshold_impl`

void `dont_care`
result_type `Args const &`
Args const &
accumulator_base
Quantile Estimation based on Peaks over Threshold Method (for both left and right tails)

Computes an estimate

#

\[ \hat{q}_{\alpha} = \bar{u} + \frac{\bar{\beta}}{\xi}\left[(1-\alpha)^{-\xi}-1\right] \]\[ \hat{q}_{\alpha} = \bar{u} + \frac{\bar{\beta}}{\xi}\left[(1-\alpha)^{-\xi}-1\right] \] for a right or left extreme quantile, $\bar[u]$$\bar[u]$, $\bar{\beta}$$\bar{\beta}$ and $\xi$$\xi$ being the parameters of the generalized Pareto distribution that approximates the right tail of the distribution (or the mirrored left tail, in case the left tail is used). In the latter case, the result is mirrored back, yielding the correct result.
result_type `Args const &`
dont_care
accumulator_base
Estimation of the (coherent) tail mean based on the peaks over threshold method (for both left and right tails)

Computes an estimate for the (coherent) tail mean

#

\[ \widehat{CTM}_{\alpha} = \hat{q}_{\alpha} - \frac{\bar{\beta}}{\xi-1}(1-\alpha)^{-\xi}, \]\[ \widehat{CTM}_{\alpha} = \hat{q}_{\alpha} - \frac{\bar{\beta}}{\xi-1}(1-\alpha)^{-\xi}, \] where $\bar[u]$$\bar[u]$, $\bar{\beta}$$\bar{\beta}$ and $\xi$$\xi$ are the parameters of the generalized Pareto distribution that approximates the right tail of the distribution (or the mirrored left tail, in case the left tail is used). In the latter case, the result is mirrored back, yielding the correct result.
result_type `Args const &`
dont_care
accumulator_base
Histogram calculation of the cumulative distribution with the $P^2$$P^2$ algorithm.

A histogram of the sample cumulative distribution is computed dynamically without storing samples based on the $ P^2 $$ P^2 $ algorithm. The returned histogram has a specifiable amount (num_cells) equiprobable (and not equal-sized) cells.

For further details, see

R. Jain and I. Chlamtac, The P^2 algorithm for dynamic calculation of quantiles and histograms without storing observations, Communications of the ACM, Volume 28 (October), Number 10, 1985, p. 1076-1085.

void `Args const &`
result_type `Args const &`
Args const &
accumulator_base
Single quantile estimation with the $P^2$$P^2$ algorithm.

The $P^2$$P^2$ algorithm estimates a quantile dynamically without storing samples. Instead of storing the whole sample cumulative distribution, only five points (markers) are stored. The heights of these markers are the minimum and the maximum of the samples and the current estimates of the $(p/2)$$(p/2)$-, $p$$p$- and $(1+p)/2$$(1+p)/2$-quantiles. Their positions are equal to the number of samples that are smaller or equal to the markers. Each time a new samples is recorded, the positions of the markers are updated and if necessary their heights are adjusted using a piecewise- parabolic formula.

For further details, see

R. Jain and I. Chlamtac, The P^2 algorithm for dynamic calculation of quantiles and histograms without storing observations, Communications of the ACM, Volume 28 (October), Number 10, 1985, p. 1076-1085.

void `Args const &`
result_type `dont_care`
Args const &
accumulator_base
Skewness estimation.

The skewness of a sample distribution is defined as the ratio of the 3rd central moment and the $ 3/2 $$ 3/2 $-th power of the 2nd central moment (the variance) of the samples 3. The skewness can also be expressed by the simple moments:

#

\[ \hat{g}_1 = \frac {\widehat{m}_n^{(3)}-3\widehat{m}_n^{(2)}\hat{\mu}_n+2\hat{\mu}_n^3} {\left(\widehat{m}_n^{(2)} - \hat{\mu}_n^{2}\right)^{3/2}} \]\[ \hat{g}_1 = \frac {\widehat{m}_n^{(3)}-3\widehat{m}_n^{(2)}\hat{\mu}_n+2\hat{\mu}_n^3} {\left(\widehat{m}_n^{(2)} - \hat{\mu}_n^{2}\right)^{3/2}} \]

where $ \widehat{m}_n^{(i)} $$ \widehat{m}_n^{(i)} $ are the $ i $$ i $-th moment and $ \hat{\mu}_n $$ \hat{\mu}_n $ the mean (first moment) of the $ n $$ n $ samples.

result_type `Args const &`
dont_care
tag::sample
accumulator_base
void `Args const &`
result_type `dont_care`
Args const &
accumulator_base
void `Args const &`
result_type `dont_care`
Args const &
Kahan summation algorithm.

The Kahan summation algorithm reduces the numerical error obtained with standard sequential sum.

accumulator_base
(mpl::or_< is_same< LeftRight, `right` >, is_same< LeftRight, `left` > >)
void `Args const &`
result_type `dont_care`
Args const &
tail_impl
const &
void `Args const &` `std::size_t`
accumulator_base
Estimation of the coherent tail mean based on order statistics (for both left and right tails)

The coherent tail mean $\widehat{CTM}_{n,\alpha}(X)$$\widehat{CTM}_{n,\alpha}(X)$ is equal to the non-coherent tail mean $\widehat{NCTM}_{n,\alpha}(X)$$\widehat{NCTM}_{n,\alpha}(X)$ plus a correction term that ensures coherence in case of non-continuous distributions.

#

\[ \widehat{CTM}_{n,\alpha}^{\mathrm{right}}(X) = \widehat{NCTM}_{n,\alpha}^{\mathrm{right}}(X) + \frac{1}{\lceil n(1-\alpha)\rceil}\hat{q}_{n,\alpha}(X)\left(1 - \alpha - \frac{1}{n}\lceil n(1-\alpha)\rceil \right) \]\[ \widehat{CTM}_{n,\alpha}^{\mathrm{right}}(X) = \widehat{NCTM}_{n,\alpha}^{\mathrm{right}}(X) + \frac{1}{\lceil n(1-\alpha)\rceil}\hat{q}_{n,\alpha}(X)\left(1 - \alpha - \frac{1}{n}\lceil n(1-\alpha)\rceil \right) \]

#

\[ \widehat{CTM}_{n,\alpha}^{\mathrm{left}}(X) = \widehat{NCTM}_{n,\alpha}^{\mathrm{left}}(X) + \frac{1}{\lceil n\alpha\rceil}\hat{q}_{n,\alpha}(X)\left(\alpha - \frac{1}{n}\lceil n\alpha\rceil \right) \]\[ \widehat{CTM}_{n,\alpha}^{\mathrm{left}}(X) = \widehat{NCTM}_{n,\alpha}^{\mathrm{left}}(X) + \frac{1}{\lceil n\alpha\rceil}\hat{q}_{n,\alpha}(X)\left(\alpha - \frac{1}{n}\lceil n\alpha\rceil \right) \]
result_type `Args const &`
dont_care
accumulator_base
Estimation of the (non-coherent) tail mean based on order statistics (for both left and right tails)

An estimation of the non-coherent tail mean $\widehat{NCTM}_{n,\alpha}(X)$$\widehat{NCTM}_{n,\alpha}(X)$ is given by the mean of the $\lceil n\alpha\rceil$$\lceil n\alpha\rceil$ smallest samples (left tail) or the mean of the $\lceil n(1-\alpha)\rceil$$\lceil n(1-\alpha)\rceil$ largest samples (right tail), $n$$n$ being the total number of samples and $\alpha$$\alpha$ the quantile level:

#

\[ \widehat{NCTM}_{n,\alpha}^{\mathrm{right}}(X) = \frac{1}{\lceil n(1-\alpha)\rceil} \sum_{i=\lceil \alpha n \rceil}^n X_{i:n} \]\[ \widehat{NCTM}_{n,\alpha}^{\mathrm{right}}(X) = \frac{1}{\lceil n(1-\alpha)\rceil} \sum_{i=\lceil \alpha n \rceil}^n X_{i:n} \]

#

\[ \widehat{NCTM}_{n,\alpha}^{\mathrm{left}}(X) = \frac{1}{\lceil n\alpha\rceil} \sum_{i=1}^{\lceil \alpha n \rceil} X_{i:n} \]\[ \widehat{NCTM}_{n,\alpha}^{\mathrm{left}}(X) = \frac{1}{\lceil n\alpha\rceil} \sum_{i=1}^{\lceil \alpha n \rceil} X_{i:n} \]

It thus requires the caching of at least the $\lceil n\alpha\rceil$$\lceil n\alpha\rceil$ smallest or the $\lceil n(1-\alpha)\rceil$$\lceil n(1-\alpha)\rceil$ largest samples.

result_type `Args const &`
dont_care
accumulator_base
Tail quantile estimation based on order statistics (for both left and right tails)

The estimation of a tail quantile $\hat{q}$$\hat{q}$ with level $\alpha$$\alpha$ based on order statistics requires the caching of at least the $\lceil n\alpha\rceil$$\lceil n\alpha\rceil$ smallest or the $\lceil n(1-\alpha)\rceil$$\lceil n(1-\alpha)\rceil$ largest samples, $n$$n$ being the total number of samples. The largest of the $\lceil n\alpha\rceil$$\lceil n\alpha\rceil$ smallest samples or the smallest of the $\lceil n(1-\alpha)\rceil$$\lceil n(1-\alpha)\rceil$ largest samples provides an estimate for the quantile:

#

\[ \hat{q}_{n,\alpha} = X_{\lceil \alpha n \rceil:n} \]\[ \hat{q}_{n,\alpha} = X_{\lceil \alpha n \rceil:n} \]

result_type `Args const &`
dont_care
accumulator_base
void `Args const &` `std::size_t`
result_type `Args const &`
Args const &
result_type `TailRng const &`
accumulator_base
Estimation of the absolute and relative tail variate means (for both left and right tails)

For all $j$$j$-th variates associated to the $\lceil n(1-\alpha)\rceil$$\lceil n(1-\alpha)\rceil$ largest samples (or the $\lceil n(1-\alpha)\rceil$$\lceil n(1-\alpha)\rceil$ smallest samples in case of the left tail), the absolute tail means $\widehat{ATM}_{n,\alpha}(X, j)$$\widehat{ATM}_{n,\alpha}(X, j)$ are computed and returned as an iterator range. Alternatively, the relative tail means $\widehat{RTM}_{n,\alpha}(X, j)$$\widehat{RTM}_{n,\alpha}(X, j)$ are returned, which are the absolute tail means normalized with the (non-coherent) sample tail mean $\widehat{NCTM}_{n,\alpha}(X)$$\widehat{NCTM}_{n,\alpha}(X)$.

#

\[ \widehat{ATM}_{n,\alpha}^{\mathrm{right}}(X, j) = \frac{1}{\lceil n(1-\alpha) \rceil} \sum_{i=\lceil \alpha n \rceil}^n \xi_{j,i} \]\[ \widehat{ATM}_{n,\alpha}^{\mathrm{right}}(X, j) = \frac{1}{\lceil n(1-\alpha) \rceil} \sum_{i=\lceil \alpha n \rceil}^n \xi_{j,i} \]

#

\[ \widehat{ATM}_{n,\alpha}^{\mathrm{left}}(X, j) = \frac{1}{\lceil n\alpha \rceil} \sum_{i=1}^{\lceil n\alpha \rceil} \xi_{j,i} \]\[ \widehat{ATM}_{n,\alpha}^{\mathrm{left}}(X, j) = \frac{1}{\lceil n\alpha \rceil} \sum_{i=1}^{\lceil n\alpha \rceil} \xi_{j,i} \]

#

\[ \widehat{RTM}_{n,\alpha}^{\mathrm{right}}(X, j) = \frac{\sum_{i=\lceil n\alpha \rceil}^n \xi_{j,i}} {\lceil n(1-\alpha)\rceil\widehat{NCTM}_{n,\alpha}^{\mathrm{right}}(X)} \]\[ \widehat{RTM}_{n,\alpha}^{\mathrm{right}}(X, j) = \frac{\sum_{i=\lceil n\alpha \rceil}^n \xi_{j,i}} {\lceil n(1-\alpha)\rceil\widehat{NCTM}_{n,\alpha}^{\mathrm{right}}(X)} \]

#

\[ \widehat{RTM}_{n,\alpha}^{\mathrm{left}}(X, j) = \frac{\sum_{i=1}^{\lceil n\alpha \rceil} \xi_{j,i}} {\lceil n\alpha\rceil\widehat{NCTM}_{n,\alpha}^{\mathrm{left}}(X)} \]\[ \widehat{RTM}_{n,\alpha}^{\mathrm{left}}(X, j) = \frac{\sum_{i=1}^{\lceil n\alpha \rceil} \xi_{j,i}} {\lceil n\alpha\rceil\widehat{NCTM}_{n,\alpha}^{\mathrm{left}}(X)} \]
result_type `Args const &`
dont_care
accumulator_base
Lazy calculation of variance.

Default sample variance implementation based on the second moment $ M_n^{(2)} $$ M_n^{(2)} $ moment<2>, mean and count.

#

\[ \sigma_n^2 = M_n^{(2)} - \mu_n^2. \]\[ \sigma_n^2 = M_n^{(2)} - \mu_n^2. \] where

#

\[ \mu_n = \frac{1}{n} \sum_{i = 1}^n x_i. \]\[ \mu_n = \frac{1}{n} \sum_{i = 1}^n x_i. \] is the estimate of the sample mean and $n$$n$ is the number of samples.
result_type `Args const &`
dont_care
accumulator_base
Iterative calculation of variance.

Iterative calculation of sample variance $\sigma_n^2$$\sigma_n^2$ according to the formula

#

\[ \sigma_n^2 = \frac{1}{n} \sum_{i = 1}^n (x_i - \mu_n)^2 = \frac{n-1}{n} \sigma_{n-1}^2 + \frac{1}{n-1}(x_n - \mu_n)^2. \]\[ \sigma_n^2 = \frac{1}{n} \sum_{i = 1}^n (x_i - \mu_n)^2 = \frac{n-1}{n} \sigma_{n-1}^2 + \frac{1}{n-1}(x_n - \mu_n)^2. \] where

#

\[ \mu_n = \frac{1}{n} \sum_{i = 1}^n x_i. \]\[ \mu_n = \frac{1}{n} \sum_{i = 1}^n x_i. \] is the estimate of the sample mean and $n$$n$ is the number of samples.

Note that the sample variance is not defined for $n <= 1$$n <= 1$.

A simplification can be obtained by the approximate recursion

#

\[ \sigma_n^2 \approx \frac{n-1}{n} \sigma_{n-1}^2 + \frac{1}{n}(x_n - \mu_n)^2. \]\[ \sigma_n^2 \approx \frac{n-1}{n} \sigma_{n-1}^2 + \frac{1}{n}(x_n - \mu_n)^2. \] because the difference

#

\[ \left(\frac{1}{n-1} - \frac{1}{n}\right)(x_n - \mu_n)^2 = \frac{1}{n(n-1)}(x_n - \mu_n)^2. \]\[ \left(\frac{1}{n-1} - \frac{1}{n}\right)(x_n - \mu_n)^2 = \frac{1}{n(n-1)}(x_n - \mu_n)^2. \] converges to zero as $n \rightarrow \infty$$n \rightarrow \infty$. However, for small $ n $$ n $ the difference can be non-negligible.
void `Args const &`
result_type `dont_care`
Args const &
accumulator_base
Weighted Covariance Estimator.

An iterative Monte Carlo estimator for the weighted covariance $\mathrm{Cov}(X,X')$$\mathrm{Cov}(X,X')$, where $X$$X$ is a sample and $X'$$X'$ a variate, is given by:

#

\[ \hat{c}_n = \frac{\bar{w}_n-w_n}{\bar{w}_n} \hat{c}_{n-1} + \frac{w_n}{\bar{w}_n-w_n}(X_n - \hat{\mu}_n)(X_n' - \hat{\mu}_n'), \quad n\ge2,\quad\hat{c}_1 = 0, \]\[ \hat{c}_n = \frac{\bar{w}_n-w_n}{\bar{w}_n} \hat{c}_{n-1} + \frac{w_n}{\bar{w}_n-w_n}(X_n - \hat{\mu}_n)(X_n' - \hat{\mu}_n'), \quad n\ge2,\quad\hat{c}_1 = 0, \]

$\hat{\mu}_n$$\hat{\mu}_n$ and $\hat{\mu}_n'$$\hat{\mu}_n'$ being the weighted means of the samples and variates and $\bar{w}_n$$\bar{w}_n$ the sum of the $n$$n$ first weights $w_i$$w_i$.

void `Args const &`
result_type `dont_care`
Args const &
accumulator_base
Histogram density estimator for weighted samples.

The histogram density estimator returns a histogram of the sample distribution. The positions and sizes of the bins are determined using a specifiable number of cached samples (cache_size). The range between the minimum and the maximum of the cached samples is subdivided into a specifiable number of bins (num_bins) of same size. Additionally, an under- and an overflow bin is added to capture future under- and overflow samples. Once the bins are determined, the cached samples and all subsequent samples are added to the correct bins. At the end, a range of std::pair is returned, where each pair contains the position of the bin (lower bound) and the sum of the weights (normalized with the sum of all weights).

void `Args const &`
result_type `Args const &`
Args const &
accumulator_base
Kurtosis estimation for weighted samples.

The kurtosis of a sample distribution is defined as the ratio of the 4th central moment and the square of the 2nd central moment (the variance) of the samples, minus 3. The term $ -3 $$ -3 $ is added in order to ensure that the normal distribution has zero kurtosis. The kurtosis can also be expressed by the simple moments:

#

\[ \hat{g}_2 = \frac {\widehat{m}_n^{(4)}-4\widehat{m}_n^{(3)}\hat{\mu}_n+6\widehat{m}_n^{(2)}\hat{\mu}_n^2-3\hat{\mu}_n^4} {\left(\widehat{m}_n^{(2)} - \hat{\mu}_n^{2}\right)^2} - 3, \]\[ \hat{g}_2 = \frac {\widehat{m}_n^{(4)}-4\widehat{m}_n^{(3)}\hat{\mu}_n+6\widehat{m}_n^{(2)}\hat{\mu}_n^2-3\hat{\mu}_n^4} {\left(\widehat{m}_n^{(2)} - \hat{\mu}_n^{2}\right)^2} - 3, \]

where $ \widehat{m}_n^{(i)} $$ \widehat{m}_n^{(i)} $ are the $ i $$ i $-th moment and $ \hat{\mu}_n $$ \hat{\mu}_n $ the mean (first moment) of the $ n $$ n $ samples.

The kurtosis estimator for weighted samples is formally identical to the estimator for unweighted samples, except that the weighted counterparts of all measures it depends on are to be taken.

result_type `Args const &`
dont_care
accumulator_base
Median estimation for weighted samples based on the $P^2$$P^2$ quantile estimator.

The $P^2$$P^2$ algorithm for weighted samples is invoked with a quantile probability of 0.5.

result_type `Args const &`
dont_care
accumulator_base
Median estimation for weighted samples based on the density estimator.

The algorithm determines the bin in which the $0.5*cnt$$0.5*cnt$-th sample lies, $cnt$$cnt$ being the total number of samples. It returns the approximate horizontal position of this sample, based on a linear interpolation inside the bin.

void `dont_care`
result_type `Args const &`
Args const &
accumulator_base
Median estimation for weighted samples based on the $P^2$$P^2$ cumulative distribution estimator.

The algorithm determines the first (leftmost) bin with a height exceeding 0.5. It returns the approximate horizontal position of where the cumulative distribution equals 0.5, based on a linear interpolation inside the bin.

void `dont_care`
result_type `Args const &`
dont_care
accumulator_base
result_type `Args const &`
dont_care
accumulator_base
void `Args const &`
result_type `dont_care`
Args const &
accumulator_base
Weighted Peaks over Threshold Method for Weighted Quantile and Weighted Tail Mean Estimation.

See Also:

`peaks_over_threshold_impl`

void `Args const &`
result_type `Args const &`
Args const &
accumulator_base
Peaks over Threshold Method for Quantile and Tail Mean Estimation.

See Also:

`weighted_peaks_over_threshold_impl`

void `dont_care`
result_type `Args const &`
Args const &
accumulator_base
Multiple quantile estimation with the extended $P^2$$P^2$ algorithm for weighted samples.

This version of the extended $P^2$$P^2$ algorithm extends the extended $P^2$$P^2$ algorithm to support weighted samples. The extended $P^2$$P^2$ algorithm dynamically estimates several quantiles without storing samples. Assume that $m$$m$ quantiles $\xi_{p_1}, \ldots, \xi_{p_m}$$\xi_{p_1}, \ldots, \xi_{p_m}$ are to be estimated. Instead of storing the whole sample cumulative distribution, the algorithm maintains only $m+2$$m+2$ principal markers and $m+1$$m+1$ middle markers, whose positions are updated with each sample and whose heights are adjusted (if necessary) using a piecewise-parablic formula. The heights of the principal markers are the current estimates of the quantiles and are returned as an iterator range.

For further details, see

K. E. E. Raatikainen, Simultaneous estimation of several quantiles, Simulation, Volume 49, Number 4 (October), 1986, p. 159-164.

The extended $ P^2 $$ P^2 $ algorithm generalizes the $ P^2 $$ P^2 $ algorithm of

R. Jain and I. Chlamtac, The P^2 algorithm for dynamic calculation of quantiles and histograms without storing observations, Communications of the ACM, Volume 28 (October), Number 10, 1985, p. 1076-1085.

void `Args const &`
result_type `dont_care`
Args const &
accumulator_base
N::value
0
void `Args const &`
result_type `Args const &`
Args const &
accumulator_base
Histogram calculation of the cumulative distribution with the $P^2$$P^2$ algorithm for weighted samples.

A histogram of the sample cumulative distribution is computed dynamically without storing samples based on the $ P^2 $$ P^2 $ algorithm for weighted samples. The returned histogram has a specifiable amount (num_cells) equiprobable (and not equal-sized) cells.

Note that applying importance sampling results in regions to be more and other regions to be less accurately estimated than without importance sampling, i.e., with unweighted samples.

For further details, see

R. Jain and I. Chlamtac, The P^2 algorithm for dynamic calculation of quantiles and histograms without storing observations, Communications of the ACM, Volume 28 (October), Number 10, 1985, p. 1076-1085.

void `Args const &`
result_type `Args const &`
Args const &
accumulator_base
Single quantile estimation with the $P^2$$P^2$ algorithm for weighted samples.

This version of the $P^2$$P^2$ algorithm extends the $P^2$$P^2$ algorithm to support weighted samples. The $P^2$$P^2$ algorithm estimates a quantile dynamically without storing samples. Instead of storing the whole sample cumulative distribution, only five points (markers) are stored. The heights of these markers are the minimum and the maximum of the samples and the current estimates of the $(p/2)$$(p/2)$-, $p$$p$ - and $(1+p)/2$$(1+p)/2$ -quantiles. Their positions are equal to the number of samples that are smaller or equal to the markers. Each time a new sample is added, the positions of the markers are updated and if necessary their heights are adjusted using a piecewise- parabolic formula.

For further details, see

R. Jain and I. Chlamtac, The P^2 algorithm for dynamic calculation of quantiles and histograms without storing observations, Communications of the ACM, Volume 28 (October), Number 10, 1985, p. 1076-1085.

void `Args const &`
result_type `dont_care`
Args const &
accumulator_base
Skewness estimation for weighted samples.

The skewness of a sample distribution is defined as the ratio of the 3rd central moment and the $ 3/2 $$ 3/2 $-th power $ of the 2nd central moment (the variance) of the samples. The skewness can also be expressed by the simple moments:

#

\[ \hat{g}_1 = \frac {\widehat{m}_n^{(3)}-3\widehat{m}_n^{(2)}\hat{\mu}_n+2\hat{\mu}_n^3} {\left(\widehat{m}_n^{(2)} - \hat{\mu}_n^{2}\right)^{3/2}} \]\[ \hat{g}_1 = \frac {\widehat{m}_n^{(3)}-3\widehat{m}_n^{(2)}\hat{\mu}_n+2\hat{\mu}_n^3} {\left(\widehat{m}_n^{(2)} - \hat{\mu}_n^{2}\right)^{3/2}} \]

where $ \widehat{m}_n^{(i)} $$ \widehat{m}_n^{(i)} $ are the $ i $$ i $-th moment and $ \hat{\mu}_n $$ \hat{\mu}_n $ the mean (first moment) of the $ n $$ n $ samples.

The skewness estimator for weighted samples is formally identical to the estimator for unweighted samples, except that the weighted counterparts of all measures it depends on are to be taken.

result_type `Args const &`
dont_care
accumulator_base
void `Args const &`
result_type `dont_care`
Args const &
accumulator_base
void `Args const &`
result_type `dont_care`
Args const &
accumulator_base
Estimation of the (non-coherent) weighted tail mean based on order statistics (for both left and right tails)

An estimation of the non-coherent, weighted tail mean $\widehat{NCTM}_{n,\alpha}(X)$$\widehat{NCTM}_{n,\alpha}(X)$ is given by the weighted mean of the

#

\[ \lambda = \inf\left\{ l \left| \frac{1}{\bar{w}_n}\sum_{i=1}^{l} w_i \geq \alpha \right. \right\} \]\[ \lambda = \inf\left\{ l \left| \frac{1}{\bar{w}_n}\sum_{i=1}^{l} w_i \geq \alpha \right. \right\} \]

smallest samples (left tail) or the weighted mean of the

#

\[ n + 1 - \rho = n + 1 - \sup\left\{ r \left| \frac{1}{\bar{w}_n}\sum_{i=r}^{n} w_i \geq (1 - \alpha) \right. \right\} \]\[ n + 1 - \rho = n + 1 - \sup\left\{ r \left| \frac{1}{\bar{w}_n}\sum_{i=r}^{n} w_i \geq (1 - \alpha) \right. \right\} \]

largest samples (right tail) above a quantile $\hat{q}_{\alpha}$$\hat{q}_{\alpha}$ of level $\alpha$$\alpha$, $n$$n$ being the total number of sample and $\bar{w}_n$$\bar{w}_n$ the sum of all $n$$n$ weights:

#

\[ \widehat{NCTM}_{n,\alpha}^{\mathrm{left}}(X) = \frac{\sum_{i=1}^{\lambda} w_i X_{i:n}}{\sum_{i=1}^{\lambda} w_i}, \]\[ \widehat{NCTM}_{n,\alpha}^{\mathrm{left}}(X) = \frac{\sum_{i=1}^{\lambda} w_i X_{i:n}}{\sum_{i=1}^{\lambda} w_i}, \]

#

\[ \widehat{NCTM}_{n,\alpha}^{\mathrm{right}}(X) = \frac{\sum_{i=\rho}^n w_i X_{i:n}}{\sum_{i=\rho}^n w_i}. \]\[ \widehat{NCTM}_{n,\alpha}^{\mathrm{right}}(X) = \frac{\sum_{i=\rho}^n w_i X_{i:n}}{\sum_{i=\rho}^n w_i}. \]

result_type `Args const &`
dont_care
accumulator_base
Tail quantile estimation based on order statistics of weighted samples (for both left and right tails)

An estimator $\hat{q}$$\hat{q}$ of tail quantiles with level $\alpha$$\alpha$ based on order statistics $X_{1:n} \leq X_{2:n} \leq\dots\leq X_{n:n}$$X_{1:n} \leq X_{2:n} \leq\dots\leq X_{n:n}$ of weighted samples are given by $X_{\lambda:n}$$X_{\lambda:n}$ (left tail) and $X_{\rho:n}$$X_{\rho:n}$ (right tail), where

#

\[ \lambda = \inf\left\{ l \left| \frac{1}{\bar{w}_n}\sum_{i=1}^{l} w_i \geq \alpha \right. \right\} \]\[ \lambda = \inf\left\{ l \left| \frac{1}{\bar{w}_n}\sum_{i=1}^{l} w_i \geq \alpha \right. \right\} \]

and

#

\[ \rho = \sup\left\{ r \left| \frac{1}{\bar{w}_n}\sum_{i=r}^{n} w_i \geq (1 - \alpha) \right. \right\}, \]\[ \rho = \sup\left\{ r \left| \frac{1}{\bar{w}_n}\sum_{i=r}^{n} w_i \geq (1 - \alpha) \right. \right\}, \]

$n$$n$ being the number of samples and $\bar{w}_n$$\bar{w}_n$ the sum of all weights.

result_type `Args const &`
dont_care
accumulator_base
Estimation of the absolute and relative weighted tail variate means (for both left and right tails)

For all $j$$j$-th variates associated to the

#

\[ \lambda = \inf\left\{ l \left| \frac{1}{\bar{w}_n}\sum_{i=1}^{l} w_i \geq \alpha \right. \right\} \]\[ \lambda = \inf\left\{ l \left| \frac{1}{\bar{w}_n}\sum_{i=1}^{l} w_i \geq \alpha \right. \right\} \]

smallest samples (left tail) or the weighted mean of the

#

\[ n + 1 - \rho = n + 1 - \sup\left\{ r \left| \frac{1}{\bar{w}_n}\sum_{i=r}^{n} w_i \geq (1 - \alpha) \right. \right\} \]\[ n + 1 - \rho = n + 1 - \sup\left\{ r \left| \frac{1}{\bar{w}_n}\sum_{i=r}^{n} w_i \geq (1 - \alpha) \right. \right\} \]

largest samples (right tail), the absolute weighted tail means $\widehat{ATM}_{n,\alpha}(X, j)$$\widehat{ATM}_{n,\alpha}(X, j)$ are computed and returned as an iterator range. Alternatively, the relative weighted tail means $\widehat{RTM}_{n,\alpha}(X, j)$$\widehat{RTM}_{n,\alpha}(X, j)$ are returned, which are the absolute weighted tail means normalized with the weighted (non-coherent) sample tail mean $\widehat{NCTM}_{n,\alpha}(X)$$\widehat{NCTM}_{n,\alpha}(X)$.

#

\[ \widehat{ATM}_{n,\alpha}^{\mathrm{right}}(X, j) = \frac{1}{\sum_{i=\rho}^n w_i} \sum_{i=\rho}^n w_i \xi_{j,i} \]\[ \widehat{ATM}_{n,\alpha}^{\mathrm{right}}(X, j) = \frac{1}{\sum_{i=\rho}^n w_i} \sum_{i=\rho}^n w_i \xi_{j,i} \]

#

\[ \widehat{ATM}_{n,\alpha}^{\mathrm{left}}(X, j) = \frac{1}{\sum_{i=1}^{\lambda}} \sum_{i=1}^{\lambda} w_i \xi_{j,i} \]\[ \widehat{ATM}_{n,\alpha}^{\mathrm{left}}(X, j) = \frac{1}{\sum_{i=1}^{\lambda}} \sum_{i=1}^{\lambda} w_i \xi_{j,i} \]

#

\[ \widehat{RTM}_{n,\alpha}^{\mathrm{right}}(X, j) = \frac{\sum_{i=\rho}^n w_i \xi_{j,i}} {\sum_{i=\rho}^n w_i \widehat{NCTM}_{n,\alpha}^{\mathrm{right}}(X)} \]\[ \widehat{RTM}_{n,\alpha}^{\mathrm{right}}(X, j) = \frac{\sum_{i=\rho}^n w_i \xi_{j,i}} {\sum_{i=\rho}^n w_i \widehat{NCTM}_{n,\alpha}^{\mathrm{right}}(X)} \]

#

\[ \widehat{RTM}_{n,\alpha}^{\mathrm{left}}(X, j) = \frac{\sum_{i=1}^{\lambda} w_i \xi_{j,i}} {\sum_{i=1}^{\lambda} w_i \widehat{NCTM}_{n,\alpha}^{\mathrm{left}}(X)} \]\[ \widehat{RTM}_{n,\alpha}^{\mathrm{left}}(X, j) = \frac{\sum_{i=1}^{\lambda} w_i \xi_{j,i}} {\sum_{i=1}^{\lambda} w_i \widehat{NCTM}_{n,\alpha}^{\mathrm{left}}(X)} \]
result_type `Args const &`
dont_care
accumulator_base
Lazy calculation of variance of weighted samples.

The default implementation of the variance of weighted samples is based on the second moment $\widehat{m}_n^{(2)}$$\widehat{m}_n^{(2)}$ (weighted_moment<2>) and the mean $ \hat{\mu}_n$$ \hat{\mu}_n$ (weighted_mean):

#

\[ \hat{\sigma}_n^2 = \widehat{m}_n^{(2)}-\hat{\mu}_n^2, \]\[ \hat{\sigma}_n^2 = \widehat{m}_n^{(2)}-\hat{\mu}_n^2, \] where $n$$n$ is the number of samples.
result_type `Args const &`
dont_care
accumulator_base
Iterative calculation of variance of weighted samples.

Iterative calculation of variance of weighted samples:

#

\[ \hat{\sigma}_n^2 = \frac{\bar{w}_n - w_n}{\bar{w}_n}\hat{\sigma}_{n - 1}^2 + \frac{w_n}{\bar{w}_n - w_n}\left(X_n - \hat{\mu}_n\right)^2 ,\quad n\ge2,\quad\hat{\sigma}_0^2 = 0. \]\[ \hat{\sigma}_n^2 = \frac{\bar{w}_n - w_n}{\bar{w}_n}\hat{\sigma}_{n - 1}^2 + \frac{w_n}{\bar{w}_n - w_n}\left(X_n - \hat{\mu}_n\right)^2 ,\quad n\ge2,\quad\hat{\sigma}_0^2 = 0. \] where $\bar{w}_n$$\bar{w}_n$ is the sum of the $n$$n$ weights $w_i$$w_i$ and $\hat{\mu}_n$$\hat{\mu}_n$ the estimate of the mean of the weighted samples. Note that the sample variance is not defined for $n <= 1$$n <= 1$.
void `Args const &`
result_type `dont_care`
Args const &
accumulator_base
void `Args const &`
bool
result_type `dont_care`
Args const &
rolling_window_plus1_impl
const &
`rolling_window_plus1_impl` & ``rolling_window_plus1_impl` const &`
accumulator_base
result_type `Args const &`
dont_care
accumulator_base
void `Args const &`
result_type `Args const &`
Args const &
accumulator_base
result_type `Args const &`
dont_care
boost::accumulators::depends_on<>
mpl::print< class ____MISSING_SPECIFIC_QUANTILE_FEATURE_IN_ACCUMULATOR_SET____ >
boost::accumulators::depends_on<>
mpl::print< class ____MISSING_SPECIFIC_TAIL_MEAN_FEATURE_IN_ACCUMULATOR_SET____ >
boost::accumulators::depends_on< count, mean, mean_of_variates< VariateType, VariateTag > >
boost::accumulators::depends_on< Feature >
boost::accumulators::depends_on< count, sum_of_variates< VariateType, VariateTag > >
boost::accumulators::depends_on< count >
int
boost::accumulators::depends_on< count >
boost::accumulators::depends_on< count >
pot_threshold_value
boost::accumulators::depends_on< count, tail< LeftRight > >
pot_threshold_probability
boost::accumulators::depends_on< peaks_over_threshold< LeftRight >, pot_quantile< LeftRight > >
boost::accumulators::depends_on< peaks_over_threshold_prob< LeftRight >, pot_quantile_prob< LeftRight > >
boost::accumulators::depends_on< peaks_over_threshold< LeftRight > >
boost::accumulators::depends_on< peaks_over_threshold_prob< LeftRight > >
boost::accumulators::depends_on<>
boost::accumulators::depends_on<>
boost::accumulators::depends_on<>
boost::accumulators::tail_cache_size_named_arg< LeftRight >
boost::accumulators::depends_on< count, quantile, non_coherent_tail_mean< LeftRight > >
boost::accumulators::depends_on< count, tail< LeftRight > >
boost::accumulators::depends_on< count, tail< LeftRight > >
boost::accumulators::depends_on< tail< LeftRight > >
boost::accumulators::depends_on< tail< LeftRight > >
boost::accumulators::depends_on< count, non_coherent_tail_mean< LeftRight >, tail_variate< VariateType, VariateTag, LeftRight > >
boost::accumulators::depends_on< count, non_coherent_tail_mean< LeftRight >, tail_variate< VariateType, VariateTag, LeftRight > >
boost::accumulators::depends_on< count, sum_of_weights, weighted_mean, weighted_mean_of_variates< VariateType, VariateTag > >
boost::accumulators::depends_on< sum_of_weights, weighted_sum_of_variates< VariateType, VariateTag > >
boost::accumulators::depends_on< sum_of_weights >
int
boost::accumulators::depends_on< count, sum_of_weights >
boost::accumulators::depends_on< sum_of_weights >
pot_threshold_value
boost::accumulators::depends_on< sum_of_weights, tail_weights< LeftRight > >
pot_threshold_probability
boost::accumulators::depends_on< weighted_peaks_over_threshold< LeftRight > >
boost::accumulators::depends_on< weighted_peaks_over_threshold_prob< LeftRight > >
boost::accumulators::depends_on< weighted_peaks_over_threshold< LeftRight >, weighted_pot_quantile< LeftRight > >
boost::accumulators::depends_on< weighted_peaks_over_threshold_prob< LeftRight >, weighted_pot_quantile_prob< LeftRight > >
boost::accumulators::depends_on< sum_of_weights, tail_weights< LeftRight > >
boost::accumulators::depends_on< sum_of_weights, tail_weights< LeftRight > >
boost::accumulators::depends_on< non_coherent_weighted_tail_mean< LeftRight >, tail_variate< VariateType, VariateTag, LeftRight >, tail_weights< LeftRight > >
boost::accumulators::depends_on< non_coherent_weighted_tail_mean< LeftRight >, tail_variate< VariateType, VariateTag, LeftRight >, tail_weights< LeftRight > >
boost::accumulators::depends_on<>