Minimum Kolmogorov Distance and Minimum Blended phi-divergence Estimators
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F06%3A00164488" target="_blank" >RIV/68407700:21340/06:00164488 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Minimum Kolmogorov Distance and Minimum Blended phi-divergence Estimators
Original language description
We show that the approximate minimum distance estimator (ADE) exists if certain conditions are fulfiIled and that the approximate minimum Kolmogorov distance estimator (AKE) always exists. We define the robustness of ADE and we prove that AKE is always arobust estimator of the true density. Minimum Kolmogorov distance estimates produce estimators consistent in the L1-norm under weaker conditions than in cases of some other types of estimators. We used our simulation software to examine the behavior ofADE in comparison with standard estimators known for their good statistical properties.
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
CEP classification
BA - General mathematics
OECD FORD branch
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Result continuities
Project
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Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2006
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů