da Silva-Junior, A.H.M. and Einbeck, J. and Craig, P.S. (2016) 'Gradient test for generalised linear models with random effects.', in Proceedings of the 31st International Workshop on Statistical Modelling. July 4-8, 2016, Rennes, France. , pp. 213-218.
This work develops the gradient test for parameter selection in generalised linear models with random effects. Asymptotically, the test statistic has a chi-squared distribution and the statistic has a compelling feature: it does not require computation of the Fisher information matrix. Performance of the test is verified through Monte Carlo simulations of size and power, and also compared to the likelihood ratio, Wald and Rao tests. The gradient test provides the best results overall when compared to the traditional tests, especially for smaller sample sizes.
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|Record Created:||28 Jul 2016 11:35|
|Last Modified:||29 Jul 2016 13:52|
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