Implements multivariate Fay-Herriot models for small area estimation. It uses empirical best linear unbiased prediction (EBLUP) estimator. Multivariate models consider the correlation of several target variables and borrow strength from auxiliary variables to improve the effectiveness of a domain sample size. Models which accommodated by this package are univariate model with several target variables (model 0), multivariate model (model 1), autoregressive multivariate model (model 2), and heteroscedastic autoregressive multivariate model (model 3). Functions provide EBLUP estimators and mean squared error (MSE) estimator for each model. These models were developed by Roberto Benavent and Domingo Morales (2015) <doi:10.1016/j.csda.2015.07.013>.
| Version: | 0.1.2 |
| Depends: | R (≥ 2.10) |
| Imports: | magic |
| Published: | 2020-06-05 |
| Author: | Novia Permatasari, Azka Ubaidillah |
| Maintainer: | Novia Permatasari <16.9335 at stis.ac.id> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| CRAN checks: | msae results |
| Reference manual: | msae.pdf |
| Package source: | msae_0.1.2.tar.gz |
| Windows binaries: | r-devel: msae_0.1.2.zip, r-release: msae_0.1.2.zip, r-oldrel: msae_0.1.2.zip |
| macOS binaries: | r-release: msae_0.1.2.tgz, r-oldrel: msae_0.1.2.tgz |
| Old sources: | msae archive |
Please use the canonical form https://CRAN.R-project.org/package=msae to link to this page.