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统计学系系列讲座之314-315期

统计学系系列讲座之314期

 

时间:2018年7月18日(周三)9:30-10:30

地点:史带楼504室

主持人:朱仲义 教授 复旦大学管理学院统计学系

主题:Semiparametric Estimation in Case-Control Studies

主讲人:Prof. Yanyuan Ma University of Pennsylvania

 

简介:Dr Ma is Professor of Statistics in Penn State University. Research interests are Measurement error models, Dimension reduction, Mixed sample problems, Latent variable models, Selection bias and Skew-elliptical distributions, Semiparametrics, She has published about 100 Journal papers.

 

摘要:We study the regression relationship among covariates in case-control data, an area known as the secondary analysis of case-control studies. The context is such that only the form of the regression mean is specified, so that we allow an arbitrary regression error distribution, which can depend on the covariates and thus can be heteroscedastic. Under mild regularity conditions we establish the theoretical identifiability of such models. Previous work in this context has either (a) specified a fully parametric distribution for the regression errors, (b) specified a homoscedastic distribution for the regression errors, (c) has specified the rate of disease in the population (we refer this as true population), or (d) has made a rare disease approximation. We construct a class of semiparametric estimation procedures that rely on none of these. The estimators differ from the usual semiparametric ones in that they draw conclusions about the true population, while technically operating in a hypothetic superpopulation. We also construct estimators with a unique feature, in that they are robust against the misspecification of the regression error distribution in terms of variance structure, while all other nonparametric effects are estimated despite of the biased samples. We establish the asymptotic properties of the estimators and illustrate their finite sample performance through simulation studies and data applications.

 

 

统计学系系列讲座之315期

 

时间:2018年7月18日(周三)10:30-11:30

地点:史带楼504室

主持人:朱仲义 教授 复旦大学管理学院统计学系

主题:Quantile Decision Trees and Forest with its application for predicting the risk (Post-Traumatic Stress Disorder) PTSD after experienced an acute coronary syndrome

主讲人:Prof. Ying Wei Columbia University

 

简介:Dr. Ying Wei's research interests are in the general area of quantile regression, longitudinal data, and semi-parametric models, with a focus on developing methodologies of longitudinal growth chart construction. This screening process can provide an individual's current growth status by taking into account one's personal profiles. Dr. Wei's methodologies provide flexibility by avoiding underlying distribution assumption and accommodate unequally-spaced measurement time spacings. She also has investigated effective methods to make inferences, diagnose model goodness-of-fit, and assess uncertainty of screening based on the estimated models.

 

摘要:Classification and regression trees (CART) are a classic statistical learning method that efficiently partitions the sample space into mutually exclusive subspaces with the distinctive means of an outcome of interest. It is a powerful tool for efficient subgroup analysis and allows for complex associations and interactions to achieve high prediction accuracy and stability. Hence, they are appealing tools for precision health applications that deal with large amounts of data from EMRs, genomics, and mobile data and aim to provide a transparent decision mechanism. Although there is a vast literature on decision trees and random forests, most algorithms identify subspaces with distinctive outcome means. The most vulnerable or high-risk groups for certain diseases are often patients with extremely high (or low) biomarker and phenotype values. However, means-based partitioning may not be effective for identifying patients with extreme phenotype values. We propose a new regression tree framework based on quantile regression \cite{KoenkerBassett1978} that partitions the sample space and predicts the outcome of interest based on conditional quantiles of the outcome variable. We implemented and evaluated the performance of the conditional quantile trees/forests to predict the risk of developing PTSD after experiencing an acute coronary syndrome (ACS), using an observational cohort data from the REactions to Acute Care and Hospitalization (REACH) study\cite{onge2017depressive} at New York Presbyterian Hospital. The results show that the conditional quantile based trees/forest have better discrimination power to identify patients with severe PTSD symptoms, in comparison to the classical mean based CART. This is joint work with Huichen Zhu and Ian Kronish.

 

 

 

                                                           统计学系 

2018-7-16

 

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