Bayesian cohort models for general cohort table analyses
Bayesian cohort models for general cohort table analyses
复制标题
用于一般队列表分析的贝叶斯队列模型
DOI：
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发表时间：
1986
期刊：
影响因子：
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通讯作者：
Takashi Nakamura
中科院分区：
文献类型：
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作者：
Takashi Nakamura
本文提出了一种新的贝叶斯队列模型来解决队列分析中的识别问题。首先，解释了基本的队列模型，它代表了时间序列社会调查数据的统计结构，在年龄，时期和队列效应。二项分布定性数据的Logit队列模型和正态分布定量数据的正态型队列模型被认为是基本模型的两个特例。为了克服队列分析中的识别问题，采用贝叶斯方法，基于效应参数逐渐变化的假设。引入贝叶斯信息准则ABIC用于最优模型的选择。这种方法非常灵活，logit和正态型队列模型都可以适用，不仅适用于标准队列表，而且适用于年龄组范围不等于时期间隔的一般队列表。所提出的模型的实际效用表明，通过分析两个数据集的文献队列分析。
SummaryNew 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.