Aligned rank tests for the linear model with heteroscedastic errors

Michael G. Akritas; Willem Albers

doi:10.1016/0378-3758(93)90077-J

Aligned rank tests for the linear model with heteroscedastic errors

Michael G. Akritas, Willem Albers

Statistics

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3 Scopus citations

Abstract

We consider the problem of testing subhypotheses in a heteroscedastic linear regression model. The proposed test statistics are based on the ranks of scaled residuals obtained under the null hypothesis. Any estimator that is n^{1 2} -consistent under the null hypothesis can be used to form the residuals. The error variances are estimated through a parametric model. This extends the theory of aligned rank tests to the heteroscedastic linear model. A real data set is used to illustrate the procedure.

Original language	English (US)
Pages (from-to)	23-41
Number of pages	19
Journal	Journal of Statistical Planning and Inference
Volume	37
Issue number	1
DOIs	https://doi.org/10.1016/0378-3758(93)90077-J
State	Published - Oct 1993

All Science Journal Classification (ASJC) codes

Statistics and Probability
Statistics, Probability and Uncertainty
Applied Mathematics

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AB - We consider the problem of testing subhypotheses in a heteroscedastic linear regression model. The proposed test statistics are based on the ranks of scaled residuals obtained under the null hypothesis. Any estimator that is n 1 2 -consistent under the null hypothesis can be used to form the residuals. The error variances are estimated through a parametric model. This extends the theory of aligned rank tests to the heteroscedastic linear model. A real data set is used to illustrate the procedure.

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Aligned rank tests for the linear model with heteroscedastic errors

Abstract

All Science Journal Classification (ASJC) codes

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