Generalized divergence criteria for model selection between random walk and AR(1) model

Abstract : We investigate a general class of divergence measures among distributions for model selection. As alternative to the classical test of model choice, we introduce kernel type estimators of \alpha-divergence for continuous distributions based on model selection criteria in general non parametric case. We introduce the Divergence Indicator DI method by proposing a test for choosing between a random walk and a regression one, using a unified divergence measure. Under the assumptions of standard type about model densities, the asymptotic properties estimator of the expected divergence between the true unknown model and the candidate model are established. From the point of the resulting statistics divergence estimator, the performance of the discrepancy criteria is discussed and illustrated in various settings in model selection test.
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Pré-publication, Document de travail
2015
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https://hal.inria.fr/hal-01207476
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  • HAL Id : hal-01207476, version 1

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Papa Ngom, Hamza Dhaker, Mendy Pierre, El Hadji Deme. Generalized divergence criteria for model selection between random walk and AR(1) model. 2015. 〈hal-01207476〉

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