Multivariate Survival Analysis via Model Selection

• 발행기관: 한국자료분석학회
• 저자명: 조건호(Geon-Ho Cho)
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.8 No.4, 1291~1301쪽, 전체 11쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2006.08.30

영문 초록

Recently, several frailty models have been developed and widely used for analyzing multivariate survival data. Generally, selecting a suitable model among a set of candidate models is very important in data analysis. In this paper we propose a model selection criterion for semi-parametric frailty models. The proposed method is based on the conditional likelihood, a component of hierarchical likelihood and is an extension of conditional Akaike information(Vaida and Blanchard, 2005) for mixed linear models to the frailty models. The proposed method is illustrated using a well-known real data set. Here we show how to select and analyze an appropriate model

목차

1. Introduction

2. A Selection Criterion for Frailty Models

3. Example

4. Discussion

References

키워드

Conditional Akaike information criterion Conditional likelihood Frailty models Hierarchical likelihood Marginal likelihood Multivariate survival data