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  • As was already mentioned in many publications, the well known information-theoretic measures of divergence of probability measures cannot be directly applied in statistical estimation, since the divergence between the theoretical absolutely continuous probability measure and the discrete empirical probability measure is always equal to its upper bound and often takes on infinite values. In 2008 - 2009 Broniatowski & Vajda studied and extended two different modifications of divergences proposed independently in 2006. They altered the traditional phi-divergences into subdivergences and superdivergences and defined maximum subdivergence estimators with escort parameter theta and minimum superdivergence estimators. We shall present the key results of extensive simulation study of these types of point estimators. The main interest of our research was to examine these modifications in practical use as to the consistency, robustness and efficiency of the estimators.
  • As was already mentioned in many publications, the well known information-theoretic measures of divergence of probability measures cannot be directly applied in statistical estimation, since the divergence between the theoretical absolutely continuous probability measure and the discrete empirical probability measure is always equal to its upper bound and often takes on infinite values. In 2008 - 2009 Broniatowski & Vajda studied and extended two different modifications of divergences proposed independently in 2006. They altered the traditional phi-divergences into subdivergences and superdivergences and defined maximum subdivergence estimators with escort parameter theta and minimum superdivergence estimators. We shall present the key results of extensive simulation study of these types of point estimators. The main interest of our research was to examine these modifications in practical use as to the consistency, robustness and efficiency of the estimators. (en)
Title
  • Modified Power Divergence Estimators and Their Performances in Normal Models
  • Modified Power Divergence Estimators and Their Performances in Normal Models (en)
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  • Modified Power Divergence Estimators and Their Performances in Normal Models
  • Modified Power Divergence Estimators and Their Performances in Normal Models (en)
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  • RIV/68407700:21340/10:00176191!RIV11-MSM-21340___
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  • 272254
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  • RIV/68407700:21340/10:00176191
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  • Divergence estimators; Numerical optimization; Efficiency; Robustness (en)
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  • [C5959F62EAC0]
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  • Ústí nad Labem
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  • Ústí nad Labem
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  • FernStat_CZ 2010 Sborník příspěvků
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  • Frýdlová, Iva
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  • Univerzita Jana Evangelisty Purkyně v Ústí nad Labem
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  • 978-80-7414-284-0
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  • 21340
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