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Rank and Signed-Rank Tests for Random Coefficient Regression Model |
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PP: 233-247 |
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doi:10.18576/jsap/050204
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Author(s) |
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Mohamed Fihri,
Amal Mellouk,
Abdelhadi Akharif,
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Abstract |
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| In this paper, we propose nonparametric locally and asymptotically optimal tests for the problem of detecting randomness
in the coefficient of a linear regression model (in the Le Cam and H´ajek sense). That is, the problem of testing the null hypothesis
of a Standard Linear Regression (SLR) model against the alternative of a Random Coefficient Regression (RCR) model. A Local
Asymptotic Normality (LAN) property, which allows for constructing locally asymptotically optimal tests, is therefore established for
RCR models in the vicinity of SLR ones. Rank and signed-rank based versions of the optimal parametric tests are provided. These tests
are optimal, most powerful and valid under a wide class of densities. A Monte-Carlo study confirms the performance of the proposed
tests. |
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