Bayesian cohort models for general cohort table analyses
type: Item
instance of: scholarly article
title: Bayesian cohort models for general cohort table analyses
zbMATH Open document ID: 0609.62139
DOI: 10.1007/BF02482523
author: Takashi Nakamura
published in: Annals of the Institute of Statistical Mathematics
publication date: 1986-01-01T00:00:00Z
review text: New Bayesian cohort models designed to resolve the identification problem in cohort analysis are proposed in this paper. At first, the basic cohort model which represents the statistical structure of time-series social survey data in terms of age, period and cohort effects is explained. The logit cohort model for qualitative data from a binomial distribution and the normal-type cohort model for quantitative data from a normal distribution are considered as two special cases of the basic model. In order to overcome the identification problem in cohort analysis, a Bayesian approach is adopted, based on the assumption that the effect parameters change gradually. A Bayesian information criterion ABIC is introduced for the selection of the optimal model. This approach is so flexible that both the logit and the normal-type cohort models can be made applicable, not only to standard cohort tables but also to general cohort tables in which the range of age group is not equal to the interval between periods. The practical utility of the proposed models is demonstrated by analysing two data sets from the literature on cohort analysis.
Mathematics Subject Classification ID: 62P10
Mathematics Subject Classification ID: 62P25
zbMATH DE Number: 3986492
zbMATH Keywords: likelihood
zbMATH Keywords: agorithm
zbMATH Keywords: APL program
zbMATH Keywords: Bayesian cohort models
zbMATH Keywords: identification problem
zbMATH Keywords: cohort analysis
zbMATH Keywords: logit cohort model
zbMATH Keywords: normal-type cohort model
zbMATH Keywords: Bayesian information criterion
zbMATH Keywords: ABIC
zbMATH Keywords: general cohort tables
MaRDI profile type: Publication
cites work: Q3924987
cites work: The Equivalence of Maximum Likelihood and Weighted Least Squares Estimate in the Exponential Family
cites work: Q3948470
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full work available at URL: https://doi.org/10.1007/bf02482523
OpenAlex ID: W2017341127
description: scientific article; zbMATH DE number 3986492