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Article Dans Une Revue Neurocomputing Année : 2011

Asymptotic properties of mixture-of-experts models


The statistical properties of the likelihood ratio test statistic (LRTS) for mixture-of-expert models are addressed in this paper. This question is essential when estimating the number of experts in the model. Our purpose is to extend the existing results for simple mixture models and mixtures of multilayer perceptrons. In this paper we first study a simple example which embodies all the difficulties arising in such models. We find that in the most general case the LRTS diverges but, with additional assumptions, the behavior of such models can be totally explicated.
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hal-00547520 , version 1 (16-12-2010)


  • HAL Id : hal-00547520 , version 1


Madalina Olteanu, Joseph Rynkiewicz. Asymptotic properties of mixture-of-experts models. Neurocomputing, 2011, 74 (9), pp.1444-1449. ⟨hal-00547520⟩
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