R/msaeDB-Package.R

In msaeDB: Difference Benchmarking for Multivariate Small Area Estimation

#' msaeDB : Multivariate Small Area Estimation with Difference Benchmarking
#'
#' 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) <doi:10.1016/j.csda.2015.07.013>. 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).
#'
#' @section Author(s):
#' Zaza Yuda Perwira, Azka Ubaidillah
#'
#' \strong{Maintainer}: Zaza Yuda Perwira \email{221710086@@stis.ac.id}
#'
#' @section Functions:
#' \describe{
#'   \item{\code{link{msaedb}}}{Produces EBLUPs, MSE, and Aggregation of Multivariate SAE with Difference Benchmarking}
#'   \item{\code{link{saedb}}}{Produces EBLUPs, MSE, and Aggregation of Univariate SAE with Difference Benchmarking}
#'   \item{\code{link{msaefh}}}{Produces EBLUPs and MSE of Multivariate SAE}
#'   \item{\code{link{saefh}}}{Produces EBLUPs and MSE of Univariate SAE}
#'   \item{\code{link{msaedbns}}}{Produces EBLUPs, MSE, and Aggregation of Multivariate SAE with Difference Benchmarking for non-sampled areas}
#'   \item{\code{link{saedbns}}}{Produces EBLUPs, MSE, and Aggregation of Univariate SAE with Difference Benchmarking for non-sampled areas}
#'   \item{\code{link{msaefhns}}}{Produces EBLUPs and MSE of Multivariate SAE for non-sampled areas}
#'   \item{\code{link{saefhns}}}{Produces EBLUPs and MSE of Univariate SAE for non-sampled areas}
#'}
#' @section Reference:
#' \itemize{
#'   \item{Benavent, Roberto & Morales, Domingo. (2016). Multivariate Fay-Herriot models for small area estimation. Computational Statistics and Data Analysis 94 2016 372-390. <doi:10.1016/j.csda.2015.07.013>.}
#'   \item{Rao, J.N.K & Molina. (2015). Small Area Estimation 2nd Edition. New York: John Wiley and Sons, Inc.}
#'   \item{Steorts, Rebecca & Ghosh, Malay. (2013). On estimation of mean square Errors of Benchmarked Empirical Bayes Estimators. Article in Statistica Sinica April 2013. <doi:10.5705/ss.2012.053>.}
#'   \item{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>.}
#'   \item{Permatasari, Novia. (2020). Pembangunan paket R pada model Fay Herriot multivariat untuk pendugaan area kecil (Bachelor Thesis). Jakarta: Polytechnic Statistics of STIS}
#' }
#'
#'
#' @docType package
#' @name msaeDB
#'
#' @import magic
#' @import MASS
#' @import stats

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