Abstract #301098

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JSM 2003 Abstract #301098
Activity Number: 86
Type: Contributed
Date/Time: Monday, August 4, 2003 : 8:30 AM to 10:20 AM
Sponsor: Section on Physical and Engineering Sciences
Abstract - #301098
Title: A New Class of Lack of Fit Tests Based on Edge Clusterings
Author(s): James W. Neill*+ and Forrest Miller
Companies: Kansas State University and Kansas State University
Address: Dept. of Statistics, Manhattan, KS, 66506,
Keywords: lack of fit ; linear regression ; edge clusters ; complementary tests
Abstract:

A clustering criterion has been previously developed (Miller, Neill and Sherfey 1998,1999 ; Neill, Miller and Brown 2002) for choosing clusters to use with Christensen's tests (1989,1991,2003) in order to determine the existence of lack of fit in linear regression models without replication. This previous work specifically addressed the problem of cluster selection for use in detecting the presence of between-cluster lack of fit. We now develop a class of complementary lack of fit tests based on edge clusterings of a graph, which can be applied in both the cases of replication and nonreplication. The advantage of these new tests is that they possess effective power for the circumstance in which there is between- or within-cluster lack of fit or a mixture of the two types of lack of fit.

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