报告题目: Nonparametric estimation of multivariate mixtures

报告人：Prof. Yichao Wu, The University of Illinois at Chicago

时间：2019年7月17日(周三)下午4:00-5:00

地点：本部维格堂119

Abstract: A multivariate mixture model is determined by three elements: the number of

components, the mixing proportions and the component distributions. Assuming that we

are given the number of components and that each mixture component has independent

marginal distributions, we propose a non-parametric method to estimate the component

distributions in a multivariate mixture model. The basic idea is that we convert the estimation

of density functions as a problem of estimating the coordinates of density functions

under a good set of basis functions. Specifically, we construct a set of basis functions by

using conditional density functions and try to recover the coordinates of component distributions under this basis. Furthermore, we show that our estimator for the component density functions is consistent. In the simulation study, we compare our algorithm with

other existing non-parametric methods of estimating component distributions in mixture

models under the assumption of conditionally independent marginals.

Yichao Wu has been a Professor of Statistics in the department of Mathematics, Statistics, and Computer Science at The University of Illinois at Chicago since August 2017. He was a faculty member in the department of Statistics at North Carolina State University from August 2008 to July 2017. He has co-authored 60+ papers in statistics journals. His research has been supported by grants from NSF and NIH including a NSF CAREER award grant. He is a fellow of American Statistical Association. He is currently serving on the editorial boards of Technometrics, Statistica Sinica, Journal of Computational and Graphical Statistics, Canadian Journal of Statistics, and Stat, and served on the editorial boards of Journal of the American Statistical Association (A&CS) and Bernoulli. 

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