sae.prop | R Documentation |
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>.
M. Rijalus Sholihin, Cucu Sumarni
Maintainer: M. Rijalus Sholihin 221810400@stis.ac.id
saeFH.uprop
EBLUPs based on a Univariate Fay Herriot model with Additive Logistic Transformation
mseFH.uprop
Parametric Bootstrap Mean Squared Error of 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
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
saeFH.mprop
EBLUPs based on a Multivariate Fay Herriot model with Additive Logistic Transformation
mseFH.mprop
Parametric Bootstrap Mean Squared Error of 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.ns.mprop
Parametric Bootstrap Mean Squared Error of EBLUPs based on a Multivariate Fay Herriot model with Additive Logistic Transformation for Non-Sampled Data
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.
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