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Maximal invariant likelihood based testing of semi-linear models

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Abstract

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.

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Authors and Affiliations

  1. Department of Econometrics and Business Statistics, Monash University, 3800, Clayton, Victoria, Australia

    Jahar L. Bhowmik & Maxwell L. King

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  1. Jahar L. Bhowmik
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  2. Maxwell L. King
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Correspondence to Maxwell L. King.

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Bhowmik, J.L., King, M.L. Maximal invariant likelihood based testing of semi-linear models. Statistical Papers 48, 357–383 (2007). https://doi.org/10.1007/s00362-006-0342-7

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  • DOI: https://doi.org/10.1007/s00362-006-0342-7

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