/tags/2023-summer/index.xml 2023 Summer - McGill Statistics Seminars

2023 Summer

  • Empirical Bayes Control of the False Discovery Exceedance

    Pallavi Basu · Aug 17, 2023

    Date: 2023-08-17

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

    Hybrid: In person / Zoom

    Location: Burnside Hall 1104

    https://mcgill.zoom.us/j/89623344755?pwd=S1E0QWVjSm8wRHdIYU5IZzllSXNjUT09

    Meeting ID: 896 2334 4755

    Passcode: 287381

    Abstract:

    In sparse large-scale testing problems where the false discovery proportion (FDP) is highly variable, the false discovery exceedance (FDX) provides a valuable alternative to the widely used false discovery rate (FDR). We develop an empirical Bayes approach to controlling the FDX. We show that for independent hypotheses from a two-group model and dependent hypotheses from a Gaussian model fulfilling the exchangeability condition, an oracle decision rule based on ranking and thresholding the local false discovery rate (lfdr) is optimal in the sense that the power is maximized subject to FDX constraint. We propose a data-driven FDX procedure that emulates the oracle via carefully designed computational shortcuts. We investigate the empirical performance of the proposed method using simulations and illustrate the merits of FDX control through an application for identifying abnormal stock trading strategies.

  • Residual-based estimation of parametric copulas under regression

    Yue Zhao · Aug 14, 2023

    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.