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Abstract Details
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Activity Number:
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623
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Type:
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Contributed
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Date/Time:
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Thursday, August 4, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #302620 |
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Title:
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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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1 Oxford Street , Cambridge, MA, 02138,
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Keywords:
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Kolmogorov-Smirnov test ;
Effective sample size ;
empirical process ;
sample autocorrelation ;
AR(1)
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Abstract:
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The Kolmogorov-Smirnov (K-S) test is designed to test an iid sample from some specified distribution. However, this test is not valid (does not have the nominal significance level) when data are autocorrelated. 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. Several simulation studies indicate that it has its nominal significance level as well as reasonable power. More importantly, it can be easily generalized to two sample test, which is a big advantage over some existing method.
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