The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

sae.prop

Implements Additive Logistic Transformation (alr) for Small Area Estimation under Fay Herriot Model. Small Area Estimation is used to borrow strength from auxiliary variables to improve the effectiveness of a domain sample size. This package uses Empirical Best Linear Unbiased Prediction (EBLUP) estimator. The Additive Logistic Transformation (alr) are based on transformation by Aitchison J (1986). The covariance matrix for multivariate application is base on covariance matrix used by Esteban M, Lombardía M, López-Vizcaíno E, Morales D, and Pérez A doi:10.1007/s11749-019-00688-w. The non-sampled models are modified area-level models based on models proposed by Anisa R, Kurnia A, and Indahwati I doi:10.9790/5728-10121519, with univariate model using model-3, and multivariate model using model-1. The MSE are estimated using Parametric Bootstrap approach. For non-sampled cases, MSE are estimated using modified approach proposed by Haris F and Ubaidillah A doi:10.4108/eai.2-8-2019.2290339.

Authors

M. Rijalus Sholihin, Cucu Sumarni

Maintainer

M. Rijalus Sholihin 221810400@stis.ac.id

Installation

You can install the released version of sae.prop from CRAN with:

install.packages("sae.prop")

Functions

• saeFH.uprop : EBLUPs based on a Univariate Fay Herriot model with Additive Logistic Transformation
• saeFH.ns.uprop : EBLUPs based on a Univariate Fay Herriot model with Additive Logistic Transformation for Non-Sampled Data
• saeFH.mprop : EBLUPs based on a Multivariate Fay Herriot model with Additive Logistic Transformation
• saeFH.ns.mprop : EBLUPs based on a Multivariate Fay Herriot model with Additive Logistic Transformation for Non-Sampled Data
• mseFH.uprop : Parametric Bootstrap Mean Squared Error of EBLUPs based on a Univariate Fay Herriot model with Additive Logistic Transformation
• mseFH.ns.uprop : Parametric Bootstrap Mean Squared Error of EBLUPs based on a Univariate Fay Herriot model with Additive Logistic Transformation for Non-Sampled Data
• mseFH.mprop : Parametric Bootstrap Mean Squared Error of EBLUPs based on a Multivariate Fay Herriot model with Additive Logistic Transformation
• mseFH.ns.mprop : Parametric Bootstrap Mean Squared Error of EBLUPs based on a Multivariate Fay Herriot model with Additive Logistic Transformation for Non-Sampled Data

References

• Rao, J.N.K & Molina. (2015). Small Area Estimation 2nd Edition. New York: John Wiley and Sons, Inc.
• Aitchison, J. (1986). The Statistical Analysis of Compositional Data. Springer Netherlands.
• Esteban, M. D., Lombardía, M. J., López-Vizcaíno, E., Morales, D., & Pérez, A. (2020). Small area estimation of proportions under area-level compositional mixed models. Test, 29(3), 793–818. https://doi.org/10.1007/s11749-019-00688-w.
• Anisa, R., Kurnia, A., & Indahwati, I. (2014). Cluster Information of Non-Sampled Area In Small Area Estimation. IOSR Journal of Mathematics, 10(1), 15–19. https://doi.org/10.9790/5728-10121519.
• Haris, F., & Ubaidillah, A. (2020, January 21). Mean Square Error of Non-Sampled Area in Small Area Estimation. https://doi.org/10.4108/eai.2-8-2019.2290339.

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.