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Abstract Details
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Activity Number:
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232
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Type:
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Topic Contributed
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Date/Time:
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Monday, July 30, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #304952 |
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Title:
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Two-Sample Inference for Temporally Dependent Functional Data
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Author(s):
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Xianyang Zhang*+ and Xiaofeng Shao
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Companies:
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University of Illinois at Urbana-Champaign and University of Illinois at Urbana-Champaign
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Address:
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725 South Wright Street, Champaign, IL, 61820, United States
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Keywords:
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Climate downscaling ;
Functional data analysis ;
Long Run Variance ;
Self-normalization ;
Time Series ;
Two sample problem
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Abstract:
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In this paper, we propose self-normalization (SN) based tests for comparing the second order properties namely, the covariance operators and their associated eigenvalues and eigenfunctions of two functional time series. The SN-based tests which are constructed based on an inconsistent long run variance estimator are shown to be more accurate in terms of the size when the functional data present temporal dependence. Under the weak dependence assumption, we study the asymptotic properties of the SN-based tests. We illustrate the finite sample properties of the proposed tests by simulation studies and apply the method to the climate projection analysis.
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