Rank tests in regression model based on minimum distance estimates

Radim Navrátil

Kybernetika (2015)

  • Volume: 51, Issue: 6, page 909-922
  • ISSN: 0023-5954

Abstract

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In this paper a new rank test in a linear regression model is introduced. The test statistic is based on a certain minimum distance estimator, however, unlike classical rank tests in regression it is not a simple linear rank statistic. Its exact distribution under the null hypothesis is derived, and further, the asymptotic distribution both under the null hypothesis and the local alternative is investigated. It is shown that the proposed test is applicable in measurement error models. Finally, a simulation study is conducted to show a good performance of the test. It has, in some situations, a greater power than the widely used Wilcoxon rank test.

How to cite

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Navrátil, Radim. "Rank tests in regression model based on minimum distance estimates." Kybernetika 51.6 (2015): 909-922. <http://eudml.org/doc/276061>.

@article{Navrátil2015,
abstract = {In this paper a new rank test in a linear regression model is introduced. The test statistic is based on a certain minimum distance estimator, however, unlike classical rank tests in regression it is not a simple linear rank statistic. Its exact distribution under the null hypothesis is derived, and further, the asymptotic distribution both under the null hypothesis and the local alternative is investigated. It is shown that the proposed test is applicable in measurement error models. Finally, a simulation study is conducted to show a good performance of the test. It has, in some situations, a greater power than the widely used Wilcoxon rank test.},
author = {Navrátil, Radim},
journal = {Kybernetika},
keywords = {measurement errors; minimum distance estimates; rank tests},
language = {eng},
number = {6},
pages = {909-922},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Rank tests in regression model based on minimum distance estimates},
url = {http://eudml.org/doc/276061},
volume = {51},
year = {2015},
}

TY - JOUR
AU - Navrátil, Radim
TI - Rank tests in regression model based on minimum distance estimates
JO - Kybernetika
PY - 2015
PB - Institute of Information Theory and Automation AS CR
VL - 51
IS - 6
SP - 909
EP - 922
AB - In this paper a new rank test in a linear regression model is introduced. The test statistic is based on a certain minimum distance estimator, however, unlike classical rank tests in regression it is not a simple linear rank statistic. Its exact distribution under the null hypothesis is derived, and further, the asymptotic distribution both under the null hypothesis and the local alternative is investigated. It is shown that the proposed test is applicable in measurement error models. Finally, a simulation study is conducted to show a good performance of the test. It has, in some situations, a greater power than the widely used Wilcoxon rank test.
LA - eng
KW - measurement errors; minimum distance estimates; rank tests
UR - http://eudml.org/doc/276061
ER -

References

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