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03- Journal of Statistics Applications & Probability
An International Journal
               
 
 
 
 
 
 
 
 
 
 
 
 

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Volumes > Vol. 5 > No. 2

 
   

Rank and Signed-Rank Tests for Random Coefficient Regression Model

PP: 233-247
doi:10.18576/jsap/050204
Author(s)
Mohamed Fihri, Amal Mellouk, Abdelhadi Akharif,
Abstract
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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