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Bayesian Double-Penalty Tobit Quantile Regression for Longitudinal Interval-Censored Data

Shikhar Tyagi, Arvind Pandey, Bhupendra Singh, Vrijesh Tripathi

1. Overview

The BDPTobitQR package implements the Bayesian Double-Penalty Tobit Quantile Regression methods for longitudinal interval-censored data as proposed by Zhao, Shu, Hu, & Luo (2024) (Mathematics, 12(12), 1782).

2. Methods

PDAL-BTQR: Double Adaptive Lasso penalty for fixed and random effects.
PDL-BTQR: Double Lasso penalty for fixed and random effects.
P-BTQR: Unpenalized Tobit quantile regression with mixed effects.

3. Usage Example

library(BDPTobitQR)

# Simulate longitudinal interval-censored data
dat <- sim_longitudinal_data(n = 15, m = 4, p = 4, seed = 123)

# Fit model
fit <- bdp_tobit_qr(
  formula = y ~ x1 + x2 + x3 + x4,
  random = ~ 1,
  data = dat,
  id = dat$id,
  lower = dat$lower,
  upper = dat$upper,
  tau = 0.5,
  method = "PDAL-BTQR"
)

summary(fit)
plot(fit)

4. References

Zhao, K., Shu, T., Hu, C., & Luo, Y. (2024). Research on Quantile Regression Method for Longitudinal Interval-Censored Data Based on Bayesian Double Penalty. Mathematics, 12(12), 1782.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.