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Nonparametric testing of closeness between two unknown distribution functions Author info | Abstract | Publisher info | Download info | Related research | Statistics Qi Li
Based on the kernel integrated square difference and applying a central limit theorem for degenerate V-statistic proposed by Hall (1984), this paper proposes a consistent nonparametric test of closeness between two unknown density functions under quite mild conditions. We only require the unknown density functions to be bounded and continuous. Monte Carlo simulations show that the proposed tests perform well for moderate sample sizes.
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Article provided by Taylor and Francis Journals in its journal Econometric Reviews .
Volume (Year): 15 (1996)
Issue (Month): 3 ()
Pages: 261-274
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Handle: RePEc:taf:emetrv:v:15:y:1996:i:3:p:261-274Contact details of provider: Web page: http://taylorandfrancis.metapress.com/link.asp?target=journal&id=107830
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Keywords: Nonparametric Test ; Unknown Densities ; Kernel Estimation ; Asymptotic Normality ; Smoothing Parameter ; Cited by : (explanations , Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile , click on "citations" and make appropriate adjustments.)
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