A Local Linear Kernel-based Test for Nonlinear Regression Models

Chin-shang Li, Ph.D.
Department of Biostatistics
St. Jude's Children's Research Hospital

Wednesday, February 8th
3:30pm
Moos 2-620
Minneapolis Campus

Abstract:

A data-driven test for the lack of fit of nonlinear regression models is proposed. This test is constructed based on comparison of local linear kernel and parametric fits, and no boundary-corrected kernels are needed at the boundary when local linear kernel fitting is used. A bandwidth is selected based on the asymptotically optimal bandwidth under the parametric null model. This selection method leads to the data-driven test. It is shown that the data-driven test statistic is asymptotically normally distributed under the null hypothesis, and the test is consistent against any fixed alternative. We study the finite-sample property of the proposed data-driven test and compare the power of the test with some existing tests through simulation studies. The practical use of the proposed test is illustrated with two real-life data sets

A social tea will be held at 3:00P.M. in A434 Mayo. All are Welcome.
For more details contact 612-624-4655 or see http://www.biostat.umn.edu/seminar_academic.html