sae.prop: Small Area Estimation using Fay-Herriot Models with Additive Logistic Transformation

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). The Additive Logistic Transformation (alr) are based on transformation by Aitchison J (1986). The covariance matrix for multivariate application is based 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>.

Version: 0.1.2
Imports: stats, utils, magic, MASS, corpcor, progress, fpc
Suggests: testthat (≥ 3.0.0)
Published: 2023-10-15
DOI: 10.32614/CRAN.package.sae.prop
Author: M. Rijalus Sholihin [aut, cre], Cucu Sumarni [aut]
Maintainer: M. Rijalus Sholihin <m.rijalussholihin at>
License: GPL-3
NeedsCompilation: no
Materials: README
CRAN checks: sae.prop results


Reference manual: sae.prop.pdf


Package source: sae.prop_0.1.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): sae.prop_0.1.2.tgz, r-oldrel (arm64): sae.prop_0.1.2.tgz, r-release (x86_64): sae.prop_0.1.2.tgz, r-oldrel (x86_64): sae.prop_0.1.2.tgz
Old sources: sae.prop archive


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