[논문]Korean Welfare Panel Data: A Computational Bayesian Method for Ordered Probit Random Effects Models

Lee, Hyejin; Kyung, Minjung

doi:10.5351/csam.2014.21.1.045

Korean Welfare Panel Data: A Computational Bayesian Method for Ordered Probit Random Effects Models 원문보기

Communications for statistical applications and methods = 한국통계학회논문집, v.21 no.1, 2014년, pp.45 - 60

Lee, Hyejin (Department of Statistics, Duksung Women's University) , Kyung, Minjung (Department of Statistics, Duksung Women's University)

Abstract ▼ AI-Helper

We introduce a MCMC sampling for a generalized linear normal random effects model with the ordered probit link function based on latent variables from suitable truncated normal distribution. Such models have proven useful in practice and we have observed numerically reasonable results in the estimation of fixed effects when the random effect term is provided. Applications that utilize Korean Welfare Panel Study data can be difficult to model; subsequently, we find that an ordered probit model with the random effects leads to an improved analyses with more accurate and precise inferences.

주제어

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문제 정의

The general focus is on household economy instability based on the deterioration of poverty and social polarization and the growth of irregular workers and youth unemployment. The goal of the study is contribute to policy and promote a policy-effect by grasping the dynamic change in household type, income, and employment of the poor and near-poor. In addtion, it is expected to contribute to policymaking and feed-back through understanding the dynamic conditions of living, the welfare supplydemand, welfare needs of each population group by income, economic activities, age and assessing policy effect.

제안 방법

In this paper, we develop algorithms for a normal random effects model with probit link for the ordered categorical responses. We adapt the truncated normal sampling of Albert and Chib (1993) for the “cutpoints” between categories.
We conduct a simulation study to evaluate the procedure for the ordered categorical outcomes with random effects.

대상 데이터

The data used here is about the self-perceived health status of 20 64 age group based on the 2012 Korean Welfare Panel Study. Fehir (1988) defined that self-perceived health status is the absence of disease, a state of well-being and the capability to function in the face of changing circumstances.

이론/모형

Political science data was considered by using a GLMM with an ordered probit link, specifically modelling the stress, from public service, of Senate-confirmed political appointees as a reason for their shout tenure. For the analysis, a semi-parametric Bayesian approach was adopted that used the Dirichlet process instead of the normal distribution to model the random effect.
This paper considered Bayesian Gibbs sampling schemes for the orderly categorized outcomes considering random effects. We studied the properties of Markov Chain Monte Carlo estimation tools for the GLMM with ordered probit link, noting that they incorporate latent information such that models fit better in the presence of unexplained heterogeneity in the data.

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