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December, 1985 Testing Linear Regression Function Adequacy without Replication
James W. Neill, Dallas E. Johnson
Ann. Statist. 13(4): 1482-1489 (December, 1985). DOI: 10.1214/aos/1176349749

Abstract

The well known pure error-lack of fit test, which can be used to assess the adequacy of a linear regression model, is generalized to accommodate the case of nonreplication. The asymptotic null distribution of the proposed test statistic is derived. Also, the proposed test statistic is shown to be asymptotically comparable under general alternatives to the test statistic obtained in the case of replication. Consistency properties associated with pseudo lack of fit and pure error mean squares are given which parallel those obtained in the case of replication. In addition, the test statistic is invariant with respect to location and scale changes made to the regression variables.

Citation

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James W. Neill. Dallas E. Johnson. "Testing Linear Regression Function Adequacy without Replication." Ann. Statist. 13 (4) 1482 - 1489, December, 1985. https://doi.org/10.1214/aos/1176349749

Information

Published: December, 1985
First available in Project Euclid: 12 April 2007

zbMATH: 0582.62056
MathSciNet: MR811504
Digital Object Identifier: 10.1214/aos/1176349749

Subjects:
Primary: 62J05
Secondary: 62F03

Rights: Copyright © 1985 Institute of Mathematical Statistics

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Vol.13 • No. 4 • December, 1985
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