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

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

3 Citations (Scopus)

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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 -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
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
Publication status	Published - 1993

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title = "Aligned rank tests for the linear model with heteroscedastic errors",

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 -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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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 -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.

U2 - 10.1016/0378-3758(93)90077-J

DO - 10.1016/0378-3758(93)90077-J

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JO - Journal of statistical planning and inference

JF - Journal of statistical planning and inference

SN - 0378-3758

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