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Activity Number: 87 - Invited ePoster Session: a Statistical Smörgåsbord
Type: Invited
Date/Time: Sunday, July 29, 2018 : 8:30 PM to 10:30 PM
Sponsor: Section on Nonparametric Statistics
Abstract #330343
Title: Functional Partial Linear Quantile Regression Based on Reproducing Kernel Hilbert Space
Author(s): Peng Liu* and Linglong KONG and Bei JIANG and Nan Zhang and Jianhua Z. Huang
Companies: University of Alberta and University of Alberta and University of Alberta and Fudan University and Texas A&M University
Keywords: functional data analysis; partial linear quantile regression; reproducing kernel hilbert space; admm

Functional and nonfunctional data are often encountered simultaneously in modern experiments for example the clinical trial as well in economics. However it's difficult to consider both data at the same time. We considered functional partial linear quantile regression in this paper where both infinite dimensional function as well as finite dimension slope parameters are included. We study the theoretical properties under a reproducing kernel Hilbert space framework which was being proved to be very flexible and powerful. Under this framework, we also developed an ADMM algorithm which is very easy to implement in practical applications.

Authors who are presenting talks have a * after their name.

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