STAT & Data Science Seminars

Seminars start at 3 pm on the following Fridays in Pickard Hall, Room 110:

Title: "Cancer prognosis analysis via integrating molecular and histopathological imaging features"

Abstract: "Modeling cancer prognosis is a “classic” yet still challenging problem. In the past two decades, high- throughput molecular data have been extensively used in such analysis. Very recently, it has been shown that histopathological imaging features, which are generated in the biopsy process, are also informative for modeling prognosis (and other outcomes/phenotypes). Molecular and imaging data contain overlapping as well as independent information. In our recent studies, we have developed regularization techniques, testing the degree of independent information for prognosis and integrating the two distinct types of data for prognosis modeling under homogeneity as well as heterogeneity."

When: September

Shuangge Ma, Ph.D.
Professor and Chair, Biostatistics Department, Yale University School of Public Health 

Title: "TBD"

Abstract: "TBD"

When: October

Andrea Baccarelli
Columbia University

Title: "TBD"

Abstract: "TBD"

When: November

Tao Wang
UT Southwestern Medical Center

Title: "Doubly Flexible Estimation under Label Shift"

Abstract: "In studies ranging from clinical medicine to policy research, complete data are usually available from a population P, but the quantity of interest is often sought for a related but different population Q which only has partial data. In this paper, we consider the setting that both outcome Y and covariate X are available from P whereas only X is available from Q, under the so-called label shift assumption, i.e., the conditional distribution of X given Y remains the same across the two populations. To estimate the parameter of interest in population Q via leveraging the information from population P, the following three ingredients are essential: (a) the common conditional distribution of X given Y , (b) the regression model of Y given X in population P, and(c) the density ratio of the outcome Y between the two populations. We propose an estimation procedure that only needs some standard nonparametric regression technique to approximate the conditional expectations with respect to (a), while by no means needs an estimate or model for (b) or (c); i.e., doubly flexible to the possible model misspecifications of both (b) and (c). This is conceptually different from the well-known doubly robust estimation in that, double robustness allows at most one model to be mis-specified whereas our proposal here can allow both (b) and (c) to be mis-specified. This is of particular interest in our setting because estimating (c) is difficult, if not impossible, by virtue of the absence of the Y -data in population Q. Furthermore, even though the estimation of (b) is sometimes off-the-shelf, it can face curse of dimensionality or computational challenges. We develop the large sample theory for the proposed estimator and examine its finite-sample performance through simulation studies as well as an application to the MIMIC-III database."

When: November

Yanyuan Ma
The Pennsylvania State University