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Modified power divergence estimators in normal models – simulation and comparative study

Iva Frýdlová;  Igor Vajda;  Václav Kůs — 2012

Kybernetika

Point estimators based on minimization of information-theoretic divergences between empirical and hypothetical distribution induce a problem when working with continuous families which are measure-theoretically orthogonal with the family of empirical distributions. In this case, the φ -divergence is always equal to its upper bound, and the minimum φ -divergence estimates are trivial. Broniatowski and Vajda [3] proposed several modifications of the minimum divergence rule to provide a solution to the...

Existence, Consistency and computer simulation for selected variants of minimum distance estimators

Václav Kůs;  Domingo Morales;  Jitka Hrabáková;  Iva Frýdlová — 2018

Kybernetika

The paper deals with sufficient conditions for the existence of general approximate minimum distance estimator (AMDE) of a probability density function f 0 on the real line. It shows that the AMDE always exists when the bounded φ -divergence, Kolmogorov, Lévy, Cramér, or discrepancy distance is used. Consequently, n - 1 / 2 consistency rate in any bounded φ -divergence is established for Kolmogorov, Lévy, and discrepancy estimators under the condition that the degree of variations of the corresponding family...

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