Yuedong Wang, Professor
Department of Statistics & Applied Probability
University of California, Santa Barbara
Nonparametric Neighborhood Selection in Graphical Models
Abstract: The neighborhood selection method directly explores the conditional dependence structure and has been widely used to construct undirected graphical models. However, there is little research on nonparametric methods for neighborhood selection with mixed data, except for some special cases with discrete data. We present a fully nonparametric neighborhood selection method under a consolidated smoothing spline ANOVA (SS ANOVA) decomposition framework. The proposed model is flexible and contains many existing models as special cases. The proposed method provides a unified framework for mixed data without any restrictions on the type of each random variable. We detect edges by applying an L1 regularization to interactions in the SS ANOVA decomposition. We propose an iterative procedure to compute the estimates and establish the convergence rates for conditional density and interactions. Simulations indicate that the proposed methods perform well under Gaussian and non-Gaussian settings. We illustrate the proposed methods using two real data examples.
Bio: I earned a bachelor's degree in Mathematics from the University of Science and Technology of China, a master's degree in Operations Research from the Institute of Applied Mathematics of the Chinese Academy of Science, and a doctoral degree in Statistics from the Department of Statistics at the University of Wisconsin-Madison. Prior to joining UCSB, I worked in the Department of Biostatistics at the University of Michigan.
I am an elected fellow of the ASA, IMS, and ISI, a fellow of RSS, and a member of IBS and ICSA. My research centers on developing statistical methodologies and exploring their diverse applications. My current interests include machine learning, nonparametric and semiparametric methods, smoothing splines, mixed-effects models, state-space models, survival analysis, longitudinal data, functional data analysis, and biostatistics.
DATE: Wednesday, April 2, 2025, 4:00 PM, AUST 202
Webex link: https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=m5f9efbf4f98750a57370ad4538e623de
Coffee will be served at 3:30 in the Noether Lounge (AUST 326)
For more information, contact: Tracy Burke at tracy.burke@uconn.edu