Date: 2023-08-14

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

Hybrid: In person / Zoom

Location: Burnside Hall 1104

https://mcgill.zoom.us/j/83436686293?pwd=b0RmWmlXRXE3OWR6NlNIcWF5d0dJQT09

Meeting ID: 834 3668 6293

Passcode: 12345

Abstract:

We study a multivariate response regression model where each coordinate is described by a location-scale regression, and where the dependence structure of the “noise” terms in the regression is described by a parametric copula. Our goal is to estimate the associated Euclidean copula parameter given a sample of the response and the covariate. In the absence of the copula sample, the oracle ranks in the usual pseudo-likelihood estimation procedure are no longer computable. Instead, we base our estimation on the residual ranks calculated from some preliminary estimators of the regression functions. We show that the residual-based estimators are asymptotically equivalent to their oracle counterparts, even when the dimension of the covariate in the regression is moderately diverging. Partially to serve this objective, we also study the weighted convergence of the residual empirical processes.

Speaker

Yue Zhao is a Lecturer in the Department of Mathematics at the University of York. His research interests lie in nonparametric and semiparametric multivariate dependence modeling (in particular, the copula) method, high-dimensional statistics, survival analysis and, more recently, statistical inference for neural networks.