Hierarchical Bayes estimation in small-area estimation using cross-sectional and time-series data

Mahmoud Torabi, Farhad Shokoohi: Hierarchical Bayes estimation in small-area estimation using cross-sectional and time-series data. In: Journal of Statistical Computation and Simulation , 84 , pp. 605-613, 2012.

Abstract

Bayesian methods have been extensively used in small area estimation. A linear model incorporating autocorrelated random effects and sampling errors was previously proposed in small area estimation using both cross-sectional and time-series data in the Bayesian paradigm. There are, however, many situations that we have time-related counts or proportions in small area estimation; for example, monthly dataset on the number of incidence in small areas. This article considers hierarchical Bayes generalized linear models for a unified analysis of both discrete and continuous data with incorporating cross-sectional and time-series data. The performance of the proposed approach is evaluated through several simulation studies and also by a real dataset.

BibTeX (Download)

@article{Shokoohi2012JSCS,
title = {Hierarchical Bayes estimation in small-area estimation using cross-sectional and time-series data},
author = {Mahmoud Torabi and Farhad Shokoohi},
doi = {10.1080/00949655.2012.721365},
year  = {2012},
date = {2012-09-11},
journal = {Journal of Statistical Computation and Simulation },
volume = {84},
pages = {605-613},
abstract = {Bayesian methods have been extensively used in small area estimation. A linear model incorporating autocorrelated random effects and sampling errors was previously proposed in small area estimation using both cross-sectional and time-series data in the Bayesian paradigm. There are, however, many situations that we have time-related counts or proportions in small area estimation; for example, monthly dataset on the number of incidence in small areas. This article considers hierarchical Bayes generalized linear models for a unified analysis of both discrete and continuous data with incorporating cross-sectional and time-series data. The performance of the proposed approach is evaluated through several simulation studies and also by a real dataset.},
keywords = {Bayesian computation, Hierarchical model, Random effects, Time series},
pubstate = {published},
tppubtype = {article}
}