SWM Kolloquium 2020

January 29th, 2020
Dr. Michael Messer, TU Wien.
Bivariate Change Point Detection - Joint Detection of Changes in Expectation or Variance.

Abstract: A method for the detection of change points in univariate sequences is presented. Particularly we focus on the detection of both changes in expectation and changes in variance. For that we exploit the joint dynamics of the empirical mean and the empirical variance in the context of moving sum statistics. The bivariate setup helps to overcome flawed change point inference as compared to separate univariate approaches. Asymptotics of the moving sum statistics support change point estimation and facilitate interpretation. Simulation studies further demonstrate strong performance.

January 22nd, 2020
Prof. Liliana Forzani,, öffnet eine externe URL in einem neuen Fenster Universidad Nacional del Litoral.
Partial Least Squares: Big Data for Chemometrics.

Abstract: Partial least squares (PLS) is one of the first methods for prediction in high-dimensional linear regressions in which the sample size need not be large relative to the number of predictors. Since its development, PLS regression has taken place mainly within the chemometrics community, where empirical prediction is the main issue, but PLS is now a core method for big data. However, studies of PLS have appeared in mainline statistics literature only from time to time and there have been no positive results on the theoretical properties of the chemometrics community's use of PLS. In a joint work with R. Dennis Cook we study the theoretical properties of prediction using PLS in the same context that chemometrics community use.