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Activity Number: 610
Type: Contributed
Date/Time: Thursday, August 7, 2014 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract #313643
Title: Nonparametric Estimation for Mixture of Functional Linear Models
Author(s): Mian Huang*+
Companies: Shanghai University of Finance and Economics
Keywords: Mixture Model ; Kernel Smoothing ; EM Algorithm ; FPCA
Abstract:

In this article, we introduce a class of mixture of functional linear models for the functional data analysis. We impose smooth structures for both the regression functions and covariances, and show that the models are identifiable under mild conditions. We propose estimation procedures using EM-type algorithm, kernel regression and functional principal component analysis. We proposed model selection using Bayesian information criterion. We used a generalized likelihood ratio test to determine whether the coefficient functions are constant. Conditional bootstrap method are examined and used for testing and standard error estimation. Numerical simulations are conducted to examine the finite sample performance of the methodologies. Finally, we use the proposed models to analyze a CO2-GDP dataset.


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