페이지 정보작성자 관리자 작성일14-05-02 09:20 조회5,303회
- 정경관_찾아오시는길.pdf (1.1M) 955회 다운로드 DATE : 2014-05-02 09:20:22
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발표자: 조현순 박사 (국립 암센터)
주제 : Bayesian Case Influence Measures and its Application to Survival models에 관한 Tutorial
일시 : 2014년 5월 16일(금)), 오후 3:30시 ~ 5시 30분
장소 : 고려대학교 정경관 506호 (약도 첨부)
The goals of assessing the influence of individual observations in statistical analysis are not only to identify influential observations such as outliers and high leverage points, but also to determine the importance of each observation in the analysis for a better model fit. Thus, assessing the influence of individual observations on a model, choosing an appropriate dimensionality of a model and selecting the best model for a given dataset are very important and highly relevant problems in any formal statistical analysis. Recently, Bayesian methodologies have been getting enormous attention in biomedical research due to the potential advantages of fitting a vast array of complex models posed by modern data. As the demand for Bayesian data analysis and modeling increases, we need good diagnostic methods for model assessment.
In this tutorial, we review Bayesian case influence measures including -divergence, Cook’s posterior mode distance and Cook’s posterior mean distance. And we will discuss Bayesian case influence diagnostics for complex survival models (e.g. the Cox model with a gamma process prior on the cumulative baseline hazard) as an example. Case deletion influence diagnostics for both the joint and marginal posterior distributions based on the Kullback–Leibler divergence and its computational formula, connection between Conditional Predictive Ordinate (CPO), diagnostics based on Cox's partial likelihood will be covered.
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