Abstract
Standard statistical theory provides us with a range of tools for testing between nested models both in the usual normal model framework and in the extended class of generalized linear models. However, in many practical data analysis problems we may wish to choose between non-nested models. This problem was first considered by Cox (1961) who obtained asymptotic results for the testing of two non-nested hypotheses based on the log-likelihood ratio statistic. Williams (1970) presented a simple simulation approach for choosing between two non-linear models. This involved obtaining distributions for the test statistic by simulating from each of the fitted models, using the parametric specification of the model, and then refitting each model to the simulated datasets to obtain values of the test statistic.
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© 1992 Springer-Verlag New York, Inc.
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Hinde, J. (1992). Choosing Between Non-nested Models: a Simulation Approach. In: Fahrmeir, L., Francis, B., Gilchrist, R., Tutz, G. (eds) Advances in GLIM and Statistical Modelling. Lecture Notes in Statistics, vol 78. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2952-0_19
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DOI: https://doi.org/10.1007/978-1-4612-2952-0_19
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