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Rank tests in regression model based on minimum distance estimates
Kybernetika, Tome 51 (2015) no. 6, pp. 909-922 Cet article a éte moissonné depuis la source Czech Digital Mathematics Library

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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.
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.
DOI : 10.14736/kyb-2015-6-0909
Classification : 62G10, 62J05
Keywords: measurement errors; minimum distance estimates; rank tests 
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     title = {Rank tests in regression model based on minimum distance estimates},
     journal = {Kybernetika},
     pages = {909--922},
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Navrátil, Radim. Rank tests in regression model based on minimum distance estimates. Kybernetika, Tome 51 (2015) no. 6, pp. 909-922. doi: 10.14736/kyb-2015-6-0909

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