Journal of Statistics Applications & Probability
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.
Suggested Reviewers
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Digital Object Identifier (DOI)
http://dx.doi.org/10.18576/jsap/050204
Recommended Citation
Fihri, Mohamed; Mellouk, Amal; and Akharif, Abdelhadi
(2016)
"Rank and Signed-Rank Tests for Random Coefficient Regression Model,"
Journal of Statistics Applications & Probability: Vol. 5:
Iss.
2, Article 4.
DOI: http://dx.doi.org/10.18576/jsap/050204
Available at:
https://digitalcommons.aaru.edu.jo/jsap/vol5/iss2/4