2018/1/5(Fri.)10:40 Nonparametric graphical models: formulation, estimation,and asymptotics

TitleNonparametric graphical models: formulation, estimation,and asymptotics

Speaker:Kuang-Yao Lee Associate Professor(Department of Statistical Science, Temple University)

Time:107/01/05/(Fri.)  10:40-11:30

Location:Room 427, Assembly Building, NCTU

Abstract

With the advance of high-throughput technologies, massive and com-plex data are routinely collected and these data need to be processed and analyzed differently from conventional data. In this presentation I will discuss a nascent concept for analyzing statistical networks — ad- ditive conditional independence (ACI) — a three-way statistical relation that shares many similarities with  conditional independence. However, its nonparametric characterization does not involve multivariate kernel, which enjoys the flexibility of nonparametric estimators but avoids the curse of dimensionality in high-dimensional settings. We facilitate the im-plementation of ACI via a case study on nonparametric graphical models, and describe a general framework for adopting ACI to a broader scope.

Additionally, to emphasize the increasing impact of ACI we also intro-duce several recent developments under various statistical settings. We investigate the properties of the proposed estimators through both theo-retical and simulation analyses. The usefulness of our procedures is also demonstrated through an application to gene regulatory network (GRN) inference using a DREAM Challenge dataset. (This is joint work with Bing Li (Penn State), Hongyu Zhao (Yale), Lexin Li (UC Berkeley)).

 

Organizer:NCTU Big Data Research Center

Co-organiser:NCTU Institute of Statistics、NTHU Institute of Statistics