Online Program Home
  My Program

Abstract Details

Activity Number: 520 - Contributed Poster Presentations: Business and Economic Statistics Section
Type: Contributed
Date/Time: Wednesday, August 2, 2017 : 10:30 AM to 12:20 PM
Sponsor: Business and Economic Statistics Section
Abstract #323340
Title: Empirical Likelihood Methods on Functional Time Series Data
Author(s): Guangxing Wang* and Wolfgang Polonik
Companies: University of California, Davis and University of California, Davis
Keywords: Empirical Likelihood ; Functional Time Series ; Empirical Likelihood Ratio ; Weakly Dependent Functional data ; Functional Principal Component Analysis ; Empirical Likelihood Confidence Region

Empirical likelihood (EL) methods play an important role in statistical inference. They combine the reliability of nonparametric methods with the effectiveness and flexibility of likelihood methods. Though there are extensive studies for finite dimensional settings, the study of EL methods on functional data is scarce. This is particularly true for dependent functional data. In this presentation, we indicate how to apply EL methods to linear functional time series data by considering a functional AR(1) model. First, we introduce an empirical maximum likelihood estimator of the kernel operator, and show its asymptotic consistency and normality. Then, we demonstrate how to utilize the asymptotic distribution of the empirical likelihood ratio to construct a confidence region for the operator.

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

Back to the full JSM 2017 program

Copyright © American Statistical Association