Bayesian Generalized Linear Models for Inference About Small Areas

Content Provider	Scilit
Author	Dey, Dipak K. Ghosh, Sujit K. Mallick, Bani K.
Copyright Year	2000
Description	Small area estimation is concerned with the estimation of parameters corresponding to small geographical areas or subpopulations when the underlying theme is to pool the data from other areas to estimate the parameters for a particular area. Interest in small area estimation has grown tremendously in recent years, more so after the elegant review paper of Ghosh and Rao (1994). More sophisticated models are being constructed to take care of many sources of variation, and these models can include both discrete data and continuous data. As can be envisioned there is a fairly large literature on continuous data models while the literature on discrete data models is very scanty. The literature on generalized linear models is relatively large, but the literature on Bayesian generalized linear models for small area estimation is limited. Book Name: Generalized Linear Models
Related Links	https://content.taylorfrancis.com/books/download?dac=C2006-0-08981-9&isbn=9780429182402&doi=10.1201/9781482293456-12&format=pdf
Ending Page	128
Page Count	22
Starting Page	107
DOI	10.1201/9781482293456-12
Language	English
Publisher	Informa UK Limited
Publisher Date	2000-05-25
Access Restriction	Open
Subject Keyword	Book Name: Generalized Linear Models Statistics and Probability Discrete Generalized Linear Models Bayesian Small Area Estimation Continuous Data
Content Type	Text
Resource Type	Chapter