Maximal invariant likelihood based testing of semi-linear modelsf

In this paper, we use a maximal invariant likelihood (MIL) to construct two likelihood ratio (LR) tests in the context of a semi-linear regression model. The first involves testing for the inclusion of a non-linear regressor and the second involves testing a linear regressor against the alternative...

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Bibliographic Details
Published inStatistical papers (Berlin, Germany) Vol. 48; no. 3; p. 357
Main Authors Bhowmik, Jahar L, King, Maxwell L
Format Journal Article
LanguageEnglish
Published Heidelberg Springer Nature B.V 01.07.2007
Subjects
Asymptotic methods
Estimates
Hypotheses
Hypothesis testing
Maximum likelihood method
Monte Carlo simulation
Regression analysis
Studies
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Summary:In this paper, we use a maximal invariant likelihood (MIL) to construct two likelihood ratio (LR) tests in the context of a semi-linear regression model. The first involves testing for the inclusion of a non-linear regressor and the second involves testing a linear regressor against the alternative of a non-linear regressor. We report the results of a Monte Carlo experiment that compares the size and power properties of the traditional LR tests with those of our proposed MIL based LR tests. Our simulation results show that in both cases, the MIL based tests have more accurate asymptotic critical values and better behaved (i.e., better centred) power curves than their classical counterparts. [PUBLICATION ABSTRACT]
ISSN:0932-5026
1613-9798
DOI:10.1007/s00362-006-0342-7