Gao, Jiti and Gijbels, Irene (2005): Bandwidth selection for nonparametric kernel testing. Forthcoming in: Journal of the American Statistical Association , Vol. 483, No. 4 (December 2008): pp. 111.

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Abstract
We propose a sound approach to bandwidth selection in nonparametric kernel testing. The main idea is to find an Edgeworth expansion of the asymptotic distribution of the test concerned. Due to the involvement of a kernel bandwidth in the leading term of the Edgeworth expansion, we are able to establish closedform expressions to explicitly represent the leading terms of both the size and power functions and then determine how the bandwidth should be chosen according to certain requirements for both the size and power functions. For example, when a significance level is given, we can choose the bandwidth such that the power function is maximized while the size function is controlled by the significance level. Both asymptotic theory and methodology are established. In addition, we develop an easy implementation procedure for the practical realization of the established methodology and illustrate this on two simulated examples and a real data example.
Item Type:  MPRA Paper 

Original Title:  Bandwidth selection for nonparametric kernel testing 
Language:  English 
Keywords:  Choice of bandwidth parameter; Edgeworth expansion; nonparametric kernel testing; power function; size function 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C14  Semiparametric and Nonparametric Methods: General 
Item ID:  11982 
Depositing User:  jiti Gao 
Date Deposited:  09. Dec 2008 00:12 
Last Modified:  13. Feb 2013 22:55 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/11982 