2021年7月7日上午9点，我院博士蒋京京为大家做线上学术报告，学院部全体研究生参加了本次线上学术报告会。

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

报告摘要：In this paper, 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.

报告会中，蒋京京博士首先指出讨论Box-Cox转换模型的推理问题时面临left-truncated和right-censored数据, 比如横断面抽样方案。然后，指出Box-Cox转换模型包含了许多常用的模型，如比例风险模型和加性风险模型等特殊情况。随后，提出了一种贝叶斯估计方法，该方法采用分段函数逼近基线风险函数。同时，利用边缘部分不受约束的条件边缘先验来解决参数约束带来的计算难题，并提出了一种MCMC抽样方法。最后对所提方法进行了有限样本性能的仿真研究，结果表明该方法适用于实际情况。使大家对于Box-Cox算法及贝叶斯估计有了更加深入的了解和兴趣。

与会人员踊跃提问，对于蒋京京博士讲解的Box-Cox转换模型进行了热烈的讨论和交流，蒋京京博士就提出的问题进行了详细的解答。本次学术交流会使大家深入了解了Box-Cox转换模型及应用，拓展了同学们的视野，聆听报告的研究生均表示受益匪浅。

数学与统计学院

2021年7月7日