Analysis of Multivariate Binary Random Effect Models using Hierarchical Likelihood Approach

• 발행기관: 한국자료분석학회
• 저자명: 김아름(Ahreum Kim) 노맹석(Maengseok Noh)
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.21 No.4, 1655~1663쪽, 전체 9쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2019.08.31

영문 초록

This paper proposes using hierarchical-likelihood estimation method for binary panel data models featuring state dependence and unmeasured heterogeneity. Hierarchical generalized linear models are an extension of generalized linear models in that they combine generalized linear models with random effects. Hierarchical likelihood based upon hierarchical generalized linear models provides a useful tool for analyzing multivariate data with correlation. These models allow various regression models for mean parameters of response variable as well as dispersion parameters. For a inference, the hierarchical- likelihood approach is suggested for a useful too as model selection and residual plots with real data analysis. In this paper, we suggest hierarchical-likelihood approach estimators for binary random effects-models, and show that this approach outperforms existing marginal likelihood estimators at low computation cost by simulation studies. And then, we add its usefulness by analyzing a real example.

목차

1. Introduction

2. The model

3. Estimation procedure

4. Simulation studies

5. Example

6. Discussions and conclusions

References

키워드

binary random effect models hierarchical generalized linear models hierarchical-likelihood marginal likelihood random effects