Abstract #300224

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JSM 2003 Abstract #300224
Activity Number: 399
Type: Invited
Date/Time: Wednesday, August 6, 2003 : 2:00 PM to 3:50 PM
Sponsor: IMS
Abstract - #300224
Title: Smoothing-Based Lack-of-Fit Tests
Author(s): Gerda Claeskens*+
Companies: Texas A&M University
Address: Dept. of Stats, 3143 TAMU, College Station, TX, 77843-0001,
Keywords: lack-of-fit tests ; power properties
Abstract:

We are interested in testing the fit of a regression function. Several tests, both parametric and nonparametric, have been constructed to test the fit of a regression function. Several properties, in particular power comparisons, of nonparametric data-driven test statistics are not yet fully developed. I will discuss asymptotic power comparisons of fully data-driven nonparametric tests using intermediate asymptotic efficiency. This approach allows us to obtain useful approximations to efficiency comparisons of two tests by specifying local alternatives that converge to the null model at nonparametric rates and simultaneously letting the level of the tests also converge to zero at a possible nonparametric rate. The smoothing parameter involved in the test statistic is determined by means of the Akaike or Bayesian information criterion or closely related measures. The test statistics under comparison are based on smooth estimators of the regression function.

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