Bayesian analysis of the Box-Cox transformation model based on left-truncated and right-censored data

  • A+

:王纯杰
:2021-08-04 10:46
:厦大海韵园实验楼105报告厅

报告人:王纯杰(长春工业大学)

 间:2021813日下午16:00

 点:厦大韵园实验楼105报告厅

内容摘要:

In this talk, we discuss the inference problem about the Box-Cox transformation model when one faces left-truncated and right-censored data, which often occur in studies, for example, involving the cross-sectional sampling scheme. It is well-known that the Box-Cox transformation model includes many commonly used models as special cases such as the proportional hazards model and the additive hazards model. For inference, a Bayesian estimation approach is proposed and in the method, the piecewise function is used to approximate the baseline hazards function. Also the conditional marginal prior, whose marginal part is free of any constraints, is employed to deal with many computational challenges caused by the constraints on the parameters, and a MCMC sampling procedure is developed. A simulation study is conducted to assess the finite sample performance of the proposed method and indicates that it works well for practical situations. We apply the approach to a set of data arising from a retirement center.

个人简介:

王纯杰,博士,教授,博士生导师,长春工业大学数学与统计学院院长,吉林省拔尖创新人才,吉林省特色高水平学首席负责人!中国概率统计研究会理事,全国工业统计学教学研究会理事,中国现场统计研究会经济与金融统计分会常务理事会,中国现场统计研究会大数据统计分会常务理事,中国现场统计研究会旅游大数据学会常务理事,吉林省工业与应用数学研究会常务理事,吉林省现场统计研究会副秘书长等。近三年主持国家自然科学基金面上项目1项,青年基金项目1项,主要参与其他各类国家自然科学基金面上项目2项,主持和参与各类省部级项目18项,发表科研论文48篇,其中SCI论文18.