Implements Benchmarking Method for Multivariate Small Area Estimation under Fay Herriot Model. Multivariate Small Area Estimation (MSAE) is a development of Univariate Small Area Estimation that considering the correlation among response variables and borrowing the strength from related areas and auxiliary variables to increase the effectiveness of sample size, the multivariate model in this package is based on multivariate model 1 proposed by Roberto Benavent and Domingo Morales (2016). Benchmarking in Small Area Estimation is a modification of Small Area Estimation model to guarantee that the aggregate weighted mean of the county predictors equals the corresponding weighted mean of survey estimates. Difference Benchmarking is the simplest benchmarking method but widely used by multiplying empirical best linear unbiased prediction (EBLUP) estimator by the common adjustment factors (J.N.K Rao and Isabel Molina, 2015).
Zaza Yuda Perwira, Azka Ubaidillah
Zaza Yuda Perwira 221710086@stis.ac.id
msaedb()
Produces EBLUPs, MSE, and Aggregation of Multivariate SAE with Difference Benchmarkingsaedb()
Produces EBLUPs, MSE, and Aggregation of Univariate SAE with Difference Benchmarkingmsaefh()
Produces EBLUPs and MSE of Multivariate SAEsaefh()
Produces EBLUPs and MSE of Univariate SAE
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- Steorts, Rebecca & Ghosh, Malay. (2013). On estimation of mean square Errors of Benchmarked Empirical Bayes Estimators. Article in Statistics Sinica April 2013. DOI: 10.5705/ss.2012.053.
- Ubaidillah, Azka et al. (2019). Multivariate Fay-Herriot models for small area estimation with application to household consumption per capita expenditure in Indonesia. Journal of Applied Statistics. 46:15. 2845-2861. DOI: 10.1080/02664763.2019.1615420.
- Permatasari, Novia. (2020). Pembangunan paket R pada model Fay Herriot multivariat untuk pendugaan area kecil (Bachelor Thesis). Jakarta: Polytechnic Statistics of STIS