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Abstract Details
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Activity Number:
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173
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #306084 |
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Title:
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A Modified Kolmogorov-Smirnov Test for Autocorrelated Data: Effective Sample Size Adjustment
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Author(s):
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Xiaojin Xu*+ and Joseph Blitzstein
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Companies:
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Harvard University and Harvard University
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Address:
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Department of Statistics, Cambridge, MA, 02138, United States
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Keywords:
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Kolmogorov-Smirnov test ;
autocorrelation ;
empirical process ;
AR(1) process ;
effective sample size
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Abstract:
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The Kolmogorov-Smirnov (K-S) test is designed to test an iid sample from a specified distribution. However, this test is not valid (does not have the claimed significance level) when there exists a sample autocorrelation. The literature of empirical process provides some theory about the asymptotic distribution of the K-S statistic when data come from an AR(1) process. Based on this, we suggest a modified K-S test with the idea of effective sample size (ESS) adjustment. It can be also easily generalized to two sample problem. Several simulation studies as well as real applications to fMRI data are provided in this paper.
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