The Department of Mathematics and Statistics invites students, faculty and staff to attend a seminar given by Jianhua Hu on Thursday, March 28th, 2024 from 2:00 PM to 3:00 PM. The talk is entitled Response best-subset selector for multivariate regression with high-dimensional response variables.

For location room number, please email Neil Marshall (nmarshall@brocku.ca).

Abstract:

This talk is about investigating the statistical problem of response-variable selection with high-dimensional response variables and a diverging number of predictor variables with respect to the sample size in the framework of multivariate linear regression. A response best-subset selection model is proposed by introducing a 0-1 selection indicator for each response variable, and then a response best-subset selector is developed by introducing a separation parameter and a novel penalized least-squares function. The proposed procedure can perform response-variable selection and regression-coefficient estimation simultaneously, and the response best-subset selector has the property of model consistency under mild conditions for both fixed and diverging numbers of predictor variables. Also, consistency and asymptotic normality of regression-coefficient estimators are established for cases with a fixed dimension, and it is found that the Bonferroni test is a special response best-subset selector. Finite-sample simulations show that the response best-subset selector has strong advantages over existing competitors in terms of the Matthews correlation coefficient, a criterion that aims to balance accuracies for both true and false response variables. An analysis of real data demonstrates the effectiveness of the response best-subset selector in an application involving the identification of dosage-sensitive genes.

This Seminar is part of the seminar series organized by the Department of Mathematics and Statistics