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 . The non-sampled models are modified area-level models based on models proposed by Anisa R, Kurnia A, and Indahwati I , 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 .

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 [email protected]

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.