Date: 2023-09-22

Time: 15:30-16:30 (Montreal time)

Location: In person, Burnside 1104

https://mcgill.zoom.us/j/89374813252

Meeting ID: 893 7481 3252

Passcode: None

Abstract:

Influential diagnosis is an integral part of data analysis, of which most existing methodological frameworks presume a deterministic submodel and are designed for low-dimensional data (i.e., the number of predictors $p$ smaller than the sample size $n$). However, the stochastic selection of a submodel from high-dimensional data where $p$ exceeds $n$ has become ubiquitous. Thus, methods for identifying observations that could exert undue influence on the choice of a submodel can play an important role in this setting. To date, discussion of this topic has been limited, falling short in two domains: (1) constrained ability to detect multiple influential points, and (2) applicability only in restrictive settings. In this talk, building on a recently proposed measure, we introduce a generalized version accommodating different model selectors, the asymptotic property of which is subsequently examined for large $p$. The $K$-means clustering is incorporated into our scheme to detect multiple influential points. Simulation is then conducted to assess the performances of various diagnostic approaches. The proposed procedure further demonstrates its value in improving predictive power when analyzing thermal-stimulated pain based on fMRI data. In addition, the latest development revolving around this newly proposed measure is also presented. This work is conducted under the joint supervision of Professors Masoud Asgharian and Martin Lindquist.

Speaker

Dongliang Zhang is a PhD candidate in the Department of Biostatistics at the Bloomberg School of Public Health, Johns Hopkins University, working under the joint supervision of Professors Martin Lindquist and Masoud Asgharian. Prior to his doctoral study, he obtained his bachelor’s and master’s degrees respectively in Honors Probability and Statistics, and Mathematics and Statistics, at the Department of Mathematics and Statistics, McGill University. His research interest revolves around large $p$ small $n$ problems with application to brain imaging data, and he is a fan of Montreal Canadiens.