Publication details
Rank tests in regression model based on minimum distance estimates
| Authors | |
|---|---|
| Year of publication | 2015 |
| Type | Article in Periodical |
| Magazine / Source | Kybernetika : The Journal of the Czech Society for Cybernetics and Informatics |
| MU Faculty or unit | |
| Citation | |
| Doi | http://dx.doi.org/10.14736/kyb-2015-6-0909 |
| Field | General mathematics |
| Keywords | measurement errors; minimum distance estimates; rank tests |
| Description | 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. |