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Teoriya Veroyatnostei i ee Primeneniya, 1999, Volume 44, Issue 3, Pages 634–645
DOI: https://doi.org/10.4213/tvp808 (Mi tvp808) 		 
This article is cited in 6 scientific papers (total in 6 papers)

Short Communications

Generalized kernel density estimator

S. Yu. Novak
Full-text PDF (655 kB) Citations (6)
DOI: https://doi.org/10.4213/tvp808
Abstract: We introduce a new class of nonparametric density estimators. It includes the classical kernel density estimator as well as the popular Abramson's estimator. We show that generalized estimators may perform much better than the classical one if the distribution has a heavy tail. The asymptotics of the mean squared error (MSE), optimal (in a sense) kernel, and smoothing parameter are found.
Keywords: kernel density estimation, square lose function, optimal kernel, optimal smoothing parameter.
Received: 12.05.1996
English version:
Theory of Probability and its Applications, 2000, Volume 44, Issue 3, Pages 570–583
DOI: https://doi.org/10.1137/S0040585X97977781
Bibliographic databases:
Document Type: Article
Language: Russian
Citation: S. Yu. Novak, “Generalized kernel density estimator”, Teor. Veroyatnost. i Primenen., 44:3 (1999), 634–645; Theory Probab. Appl., 44:3 (2000), 570–583
Citation in format AMSBIB
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\by S.~Yu.~Novak
\paper Generalized kernel density estimator
\jour Teor. Veroyatnost. i Primenen.
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\issue 3
\pages 634--645
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\transl
\jour Theory Probab. Appl.
\yr 2000
\vol 44
\issue 3
\pages 570--583
\crossref{https://doi.org/10.1137/S0040585X97977781}
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  • https://doi.org/10.4213/tvp808
  • https://www.mathnet.ru/eng/tvp/v44/i3/p634
    • This publication is cited in the following 6 articles:
    Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
    		Теория вероятностей и ее применения Theory of Probability and its Applications
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