報告題目：Bayesian transformation models with partly interval-censored data
報 告 人：王純傑 教授
所在單位：長春工業大學
報告時間：2022年9月5日 14：00-15：00
報告地點：騰訊會議 ID：533-459-182 會議密碼：0905
校内聯系人：王培潔 wangpeijie@jlu.edu.cn
報告摘要：In many scientific fields, partly interval-censored data, which consist of exactly observed and interval-censored observations on the failure time of interest, appear frequently. However, methodological developments in the analysis of partly interval-censored data are relatively limited and have mainly focused on additive or proportional hazards models. The general linear transformation model provides a highly flexible modeling framework that includes several familiar survival models as special cases. Despite such nice features, the inference procedure for this class of models has not been developed for partly interval-censored data. We propose a fully Bayesian approach coped with efficient Markov chain Monte Carlo methods to fill this gap. A four-stage data augmentation procedure is introduced to tackle the challenges presented by the complex model and data structure. The proposed method is easy to implement and computationally attractive. The empirical performance of the proposed method is evaluated through two simulation studies, and the model is then applied to a dental health study.
報告人簡介：王純傑，教授，博士生導師，現任數學與統計學院院長兼黨委副書記，吉林省拔尖創新人才，吉林省高水平優勢特色學科統計學首席負責人，統計學國家一流專業負責人，省級黃大年式統計學教學科研團隊負責人。曾先後赴美國密蘇裡大學統計系學習、香港中文大學統計系訪問，新加坡南洋理大學統計系做博士後研究。中國概率統計研究會理事，中國現場統計研究會理事，全國工業統計學教學研究會常務理事，中國商業統計學會常務理事，中國現場統計研究會大數據統計分會常務理事，吉林省現場統計研究會副秘書長等。主持國家自然科學基金面上項目1項，青年基金項目1項，主要參與國家自然科學基金面上項目2項，主持省汽車重大專項1項等，發表科研論文69篇，其中SCI論文19篇等。
