Guanyu Hu
Department of Statistics
University of Missouri
Bayesian Spatial Homogeneity Learning for Functional Data
In this talk, I will introduce two novel nonparametric Bayesian methods for learning spatial homogeneity pattern of functional data. Our methods have the advantage of effectively capturing both locally spatially contiguous clusters and globally discontinuous clusters. Posterior inferences are performed with an efficient Markov chain Monte Carlo (MCMC) algorithm. Simulation studies show that the inferences are accurate and the methods are superior compared to a wide range of competing methods. Two applications including state-level COVID-19 daily growth rates and income distributions across the US will be presented to reveal interesting findings based on proposed methods.
DATE: Thursday, 1/26/23
TIME: 3:30 PM
PLACE: AUST 105
Webex link: https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=m967b842a347c4654cb7075aeef2a03e3
Coffee will be served at 3:00 pm in the Noether Lounge (AUST 326)
For more information, contact: Tracy Burke at tracy.burke@uconn.edu