몬테칼로깁스표본기법을 이용한 누적로짓 모형의 베이지안 분석

Bayesian analysis of cumulative logit models using the Monte Carlo Gibbs sampling

  • 오만숙 ((120-750) 서울시 서대문구 대현동 이화여자대학교 통계학과)
  • 발행 : 1997.03.01

초록

순서적 다항자료의 누적로짓 모형에 대한 베이지안 사후추론을 위하여 몬테칼로 깁스표본기법을 제안하였다. 원래의 모형에서는 깁스표본기법 적용에 필수적으로 요구되는 각 원소모수의 조건부 확률분포가 난수생성에 편리한 형태로 주어지지 않으므로 Albert and Chib(1993)과 Oh(1997)에서 이항 로짓모형에 사용한 바와 같이 적절한 잠재변수를 도입하여 깁스표본기법 적용에 매우 편리한 형태를 갖도록 한다.

An easy Monte Carlo Gibbs sampling approach is suggested for Bayesian analysis of cumulative logit models for ordinal polytomous data. Because in the cumulative logit model the posterior conditional distributions of parameters are not given in convenient forms for random sample generation, appropriate latent variables are introduced into the model so that in the new model all the conditional distributions are given in very convenient forms for implementation of the Gibbs sampler.

키워드

참고문헌

  1. Analysis of Ordinal Categorical Data Agresti, A.
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