Nominate a speaker »
**Seminars will be held In Person unless otherwise noted**
Spring 2025
Seminar One
February 27, 2025
Griffin Floyd Hall, Room 100
Peijun Sang
University of Waterloo
Title:
Functional principal component analysis with informative observation times
Abstract:
Functional principal component analysis has been shown to be invaluable for revealing variation modes of longitudinal outcomes, which serves as important building blocks for forecasting and model building. Decades of research have advanced methods for functional principal component analysis often assuming independence between the observation times and longitudinal outcomes. Yet such assumptions are fragile in real-world settings where observation times may be driven by outcome-related reasons. Rather than ignoring the informative observation time process, we explicitly model the observational times by a general counting process dependent on time-varying prognostic factors. Identification of the mean, covariance function, and functional principal components ensues via inverse intensity weighting. We propose using weighted penalized splines for estimation and establish consistency and convergence rates for the weighted estimators. Simulation studies demonstrate that the proposed estimators are substantially more accurate than the existing ones in the presence of a correlation between the observation time process and the longitudinal outcome process. We further examine the finite-sample performance of the proposed method using the Acute Infection and Early Disease Research Program study.
Seminar Two
March 6, 2025
Griffin-Floyd Hall, Room 100
Alex Peterson
Brigham Young University
Title:
Modeling and Regularized Estimation of the Covariance Operator of Multivariate Functional Data
Abstract:
Functional MRI scans result in a set of regional BOLD signals for each subject, which can be modeled as multivariate functional data. The covariance operator of multivariate functional data is a complex object that can be difficult to estimate, especially if the multivariate dimension is large, making extensions of statistical methods for standard multivariate data to the functional data setting challenging. Compared with multivariate data, a key difficulty is that the covariance operator is compact and thus does not have a bounded inverse. This talk will address covariance modelling for multivariate functional data utilizing nested structures of separability, as well as regularized estimation of the functional precision operators.
Seminar Three
March 13, 2025
Zoom
Thomas Bartlett
University College, London
Title:
TBD
Abstract:
TBD
Seminar Four
March 27, 2025
Griffin-Floyd Hall, Room 100
Rajarshi Guhaniyogi
Texas A & M University
Title:
TBD
Abstract:
TBD
Seminar Five
April 3, 2025
Griffin-Floyd Hall, Room 100
Victor Patrangenaru
Florida State University
Title:
TBD
Abstract:
TBD
Seminar Six
April 17, 20215
Griffin-Floyd Hall, Room 100
Abhirup Dutta
Johns Hopkins University
Title:
TBD
Abstract:
TBD