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.