Nonparametric testing the similarity of two unknown density functions: Local power and bootstrap analysis Academic Article

abstract

• In this paper, we compare two kernel-based tests for testing the closeness between two unknown density functions, one test has a smoothing parameter h that goes to zero as sample size tends to infinity, the other one has a fixed smoothing parameter. We show that the former test (with a shrinking h) can be more powerful than the later for "singular" local alternatives considered by Rosenblatt (1975). We also demonstrate that bootstrap procedure provide a better approximation than the asymptotic normal approximation. Monte Carlo simulations are reported to examine the finite sample performances of the nonparametric kernel-based tests based on both the asymptotic and bootstrap critical values.

authors

• Li, Qi

published proceedings

• JOURNAL OF NONPARAMETRIC STATISTICS

author list (cited authors)

• Li, Q.

citation count

• 35

complete list of authors

• Li, Q

publication date

• January 1999

publisher

• Taylor & Francis  Publisher

published in

• Journal of Nonparametric Statistics  Journal

keywords

• Bootstrap Method
• Kernel Estimation
• Nonparametric Test
• Singular Local Alternative
• Smoothing Parameter
• Unknown Densities

Digital Object Identifier (DOI)

• 10.1080/10485259908832780

start page

• 189

end page

• 213

volume

• 11

issue

• 1-3

URL

• http://dx.doi.org/10.1080/10485259908832780