Empirical Likelihood for Testing Functions Constraint with Functional Data

• Activity Number: 484
• Type: Contributed
• Date/Time: Wednesday, August 7, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Honglang Wang*+ and Ping-Shou Zhong and Yuehua Cui
• Companies: Michigan State University and Michigan State University and Michigan State University
• Keywords: Functional Data ; Empirical Likelihood ; Varying Coefficient Model

Abstract

Statistical inference for functional data has been receiving large attention due to the ever increasingly observed functional data in many scientific areas. In this work, we consider the problem of hypothesis testing with functions constraint in a varying-coefficient model framework for functional data. We propose an empirical likelihood-based approach to construct the test statistic. The asymptotic distribution of the test statistic is derived under the null hypothesis and local alternatives. It is shown that the proposed test is able to detect alternatives of root-n order for dense functional data. Both simulations and real data analysis are used to demonstrate our proposed method.