報告人：李亞光 多倫多大學 博士后

報告時間：12月5號上午 9：00-9：30

報告地點：騰訊會議 ID 504 790 537

報告題目：A model‐based multithreshold method for subgroup identification

摘要：Thresholding variable plays a crucial role in subgroup identification for personalized medicine. Most existing partitioning methods split the sample based on one predictor variable. In this paper, we consider setting the splitting rule from a combination of multivariate predictors, such as the latent factors, principle components, and weighted sum of predictors. Such a subgrouping method may lead to more meaningful partitioning of the population than using a single variable. In addition, our method is based on a change point regression model and thus yields straight forward model‐based prediction results. After choosing a particular thresholding variable form, we apply a two‐stage multiple change point detection method to determine the subgroups and estimate the regression parameters. We show that our approach can produce two or more subgroups from the multiple change points and identify the true grouping with high probability. In addition, our estimation results enjoy oracle properties. We design a simulation study to compare performances of our proposed and existing methods and apply them to analyze data sets from a Scleroderma trial and a breast cancer study.

個人簡介： 李亞光，統(tǒng)計學博士。2018年11月在中國科學技術大學取得博士學位，后在多倫多大學Dalla Lana公共衛(wèi)生學院從事博士后研究，先后訪問過新加坡國立大學和約克大學。主要從事高維數(shù)據(jù)分析和個性化醫(yī)療等領域的研究。在SCIENCE CHINA-Mathematics和Statistics in Medicine等國際知名學術期刊上發(fā)表多篇論文。

報告人：張佳 西南財經(jīng)大學 博士后

報告時間：12月5號上午 9：30-10：00

報告地點：騰訊會議  ID 504 790 537

報告題目：High Dimensional Elliptical Sliced Inverse Regression in non-Gaussian Distributions

摘要：Sliced inverse regression (SIR) is the most widely-used sufficient dimension reduction method due to its simplicity, generality and computational efficiency. However, when the distribution of the covariates deviates from the multivariate normal distribution, the estimation efficiency of SIR gets rather low, and the SIR estimator may be inconsistent and misleading, especially in high-dimensional setting. In this paper, we propose a robust alternative to SIR - called elliptical sliced inverse regression (ESIR) for analyzing high-dimensional, elliptically distributed data. There are wide applications of the elliptically distributed data, especially in finance and economics where the distribution of the data is often heavy-tailed. To tackle the heavy-tailed elliptically distributed covariates, we novelly utilize the multivariate Kendall's tau matrix in a framework of generalized eigenvalue problem in sufficient dimension reduction. Methodologically, we present a practical algorithm for our method. Theoretically, we investigate the asymptotic behavior of the ESIR estimator under high-dimensional setting. Simulation results show that ESIR significantly improves the estimation efficiency in heavy-tailed scenarios. Analysis of the Istanbul stock exchange data set also demonstrates the effectiveness of our proposed method. Moreover, ESIR can be easily extended to other sufficient dimension reduction methods and applied to non-elliptical heavy-tailed distributions.

個人簡介：張佳，經(jīng)濟學博士。2019年6月在西南財經(jīng)大學取得博士學位，同年進入西南財經(jīng)大學從事博士后研究，博士后導師為常晉源教授。主要從事高維經(jīng)驗似然和充分降維等領域的研究。在CSDA、JMVA和JSPI等國際知名學術期刊上發(fā)表多篇論文。

報告人：王楊  上海交通大學  博士后

報告時間：12月5號上午 10：00-10：30

報告地點：騰訊會議 ID 504 790 537

報告題目：A Kernel Regression Model for Panel Count Data with Nonparametric Covariate Functions

摘要：Local kernel pseudo-partial likelihood is used for estimation in panel count model with nonparametric covariate functions. Estimator of the derivative of nonparametric covariate function is derived first and nonparametric function estimator is then obtained by integrating the derivative function. Under some regularity conditions, uniform consistency rates and pointwise asymptotic normality are obtained for the local derivative estimator. Moreover, the baseline function estimator is shown to be uniformly consistent. The demonstration of the asymptotic results relies strongly on the modern empirical theory, which not require the Poisson assumption. Simulation studies also show that the local derivative estimator performs well in finite-sample regardless of whether or not the Poisson assumption holds. We also apply the proposed methodology to analyze a clinical study on childhood wheezing.

個人簡介：王楊，2014年本科畢業(yè)于信陽師范學院數(shù)學系；2016年碩士畢業(yè)于浙江大學數(shù)學系；2016年至今，在上海交通大學統(tǒng)計系攻讀博士學位；2019年至2020年，在美國內布拉斯加州醫(yī)學中心訪學交流，師從面板計數(shù)數(shù)據(jù)領域的專家張殷教授。博士期間的研究方向包括面板計數(shù)數(shù)據(jù)、非參數(shù)統(tǒng)計分析、生存分析、臨床試驗研究。其中博士課題為醫(yī)學面板計數(shù)數(shù)據(jù)的非參數(shù)統(tǒng)計分析。隨著電子病歷數(shù)據(jù)的廣泛應用，面板計數(shù)數(shù)據(jù)在臨床研究應用中很常見。面板計數(shù)數(shù)據(jù)作為一種只能在固定的隨訪時間點上觀測的復發(fā)性事件數(shù)據(jù)，通常被用來研究試驗因素的固定效應，但是在臨床試驗中，試驗因素的效應往往是隨時間變化或者是非線性。此時，固定效應模型就會導致效應估計的偏差，而非參數(shù)效應模型能夠較好的分析這種時變效應或者非線性效應。因此，針對醫(yī)學面板計數(shù)數(shù)據(jù)建立非參數(shù)效應模型尤為關鍵，能夠為臨床工作者提供更為準確的醫(yī)學指導。博士期間以第一作者完成兩篇文章，一篇于2019年被Statistica Sinica接受，另一篇在Biometrics二審中。與醫(yī)生等合作完成6篇文章，其中4篇已經(jīng)發(fā)表，2篇在審稿中。

報告人：張樹雄  北京師范大學  博士

報告時間：12月5號上午 10：30-11：00

報告地點：騰訊會議 ID 504 790 537

報告題目：On large deviation probabilities for empirical distribution of the branching random walk with heavy tails

摘要：

個人簡介：張樹雄, 概率論專業(yè)博士生。2016.09-2021.07年在北京師范大學學習（碩博連讀)，師從何輝教授。研究方向為分枝隨機游動的大偏差理論，布朗運動，超過程等。