Displaying similar documents to “Divergence between various estimates of quantized information sources”

Exponential rates for the error probabilities in selection procedures

Friedrich Liese, Klaus J. Miescke (1999)

Kybernetika

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For a sequence of statistical experiments with a finite parameter set the asymptotic behavior of the maximum risk is studied for the problem of classification into disjoint subsets. The exponential rates of the optimal decision rule is determined and expressed in terms of the normalized limit of moment generating functions of likelihood ratios. Necessary and sufficient conditions for the existence of adaptive classification rules in the sense of Rukhin [Ru1] are given. The results are...

Inference about stationary distributions of Markov chains based on divergences with observed frequencies

María Luisa Menéndez, Domingo Morales, Leandro Pardo, Igor Vajda (1999)

Kybernetika

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For data generated by stationary Markov chains there are considered estimates of chain parameters minimizing φ –divergences between theoretical and empirical distributions of states. Consistency and asymptotic normality are established and the asymptotic covariance matrices are evaluated. Testing of hypotheses about the stationary distributions based on φ –divergences between the estimated and empirical distributions is considered as well. Asymptotic distributions of φ –divergence test...

On the optimal number of classes in the Pearson goodness-of-fit tests

Domingo Morales, Leandro Pardo, Igor Vajda (2005)

Kybernetika

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An asymptotic local power of Pearson chi-squared tests is considered, based on convex mixtures of the null densities with fixed alternative densities when the mixtures tend to the null densities for sample sizes n → ∞ . This local power is used to compare the tests with fixed partitions 𝒫 of the observation space of small partition sizes | 𝒫 | with the tests whose partitions 𝒫 = 𝒫 n depend on n and the partition sizes | 𝒫 n | tend to infinity for n → ∞ . New conditions are presented under which it is asymptotically...