F. El Haje Hussein, Yu. Golubev, “On entropy estimation by $m$-spacing method”, Probability and statistics. Part 14–1, Zap. Nauchn. Sem. POMI, 363, POMI, St. Petersburg, 2009, 151–181; J. Math. Sci. (N. Y.), 163:3 (2010), 290

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Zapiski Nauchnykh Seminarov POMI, 2009, Volume 363, Pages 151–181 (Mi znsl3148)

This article is cited in 7 scientific papers (total in 7 papers)

On entropy estimation by

m

$m$ -spacing method

F. El Haje Hussein^a, Yu. Golubev^bc

^a Université Rennes 2, Rennes, France
^b CNRS, Université de Provence, Marseille, France
^c Institute for Problems of Information Transmission, Moscow, Russia

Full-text PDF (290 kB) Citations (7)

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Abstract: The

m

$m$ -spacing method is a very popular statistical tool in entropy estimation and in goodness of fit testing. In this text, we focus on the case, where the underlying probability density may have an unbounded support or may vanish and show that under mild conditions the

m

$m$ -spacing entropy estimators have standard Gaussian limits. Bibl. – 15 titles.

Received: 16.12.2008

English version:
Journal of Mathematical Sciences (New York), 2010, Volume 163, Issue 3, Pages 290–309
DOI: https://doi.org/10.1007/s10958-009-9674-x

UDC: 519

Language: English

Citation: F. El Haje Hussein, Yu. Golubev, “On entropy estimation by

m

$m$ -spacing method”, Probability and statistics. Part 14–1, Zap. Nauchn. Sem. POMI, 363, POMI, St. Petersburg, 2009, 151–181; J. Math. Sci. (N. Y.), 163:3 (2010), 290–309

Citation in format AMSBIB

\Bibitem{El Gol09}

\by F.~El Haje Hussein, Yu.~Golubev

\paper On entropy estimation by $m$-spacing method

\inbook Probability and statistics. Part~14--1

\serial Zap. Nauchn. Sem. POMI

\yr 2009

\vol 363

\pages 151--181

\publ POMI

\publaddr St.~Petersburg

\mathnet{http://mi.mathnet.ru/znsl3148}

\transl

\jour J. Math. Sci. (N. Y.)

\yr 2010

\vol 163

\issue 3

\pages 290--309

\crossref{https://doi.org/10.1007/s10958-009-9674-x}

Linking options:

https://www.mathnet.ru/eng/znsl3148

https://www.mathnet.ru/eng/znsl/v363/p151

This publication is cited in the following 7 articles:

Christopher Tyler Diggans, Abd AlRahman R. AlMomani, “Geometric Partition Entropy: Coarse-Graining a Continuous State Space”, Entropy, 24:10 (2022), 1432
Yanjun Han, Jiantao Jiao, Tsachy Weissman, Yihong Wu, “Optimal rates of entropy estimation over Lipschitz balls”, Ann. Statist., 48:6 (2020)
Berrett T.B., Samworth R.J., Yuan M., “Efficient Multivariate Entropy Estimation Via K-Nearest Neighbour Distances”, Ann. Stat., 47:1 (2019), 288–318
Sergio Verdú, “Empirical Estimation of Information Measures: A Literature Guide”, Entropy, 21:8 (2019), 720
Weihao Gao, Sewoong Oh, Pramod Viswanath, “Breaking the Bandwidth Barrier: Geometrical Adaptive Entropy Estimation”, IEEE Trans. Inform. Theory, 64:5 (2018), 3313
Sylvain Delattre, Nicolas Fournier, “On the Kozachenko–Leonenko entropy estimator”, Journal of Statistical Planning and Inference, 185 (2017), 69
Jaeyong Yee, Min-Seok Kwon, Seohoon Jin, Taesung Park, Mira Park, “Detecting Genetic Interactions for Quantitative Traits Usingm-Spacing Entropy Measure”, BioMed Research International, 2015 (2015), 1

Citing articles in Google Scholar: Russian citations, English citations
Related articles in Google Scholar: Russian articles, English articles

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Abstract page:	358
Full-text PDF :	141
References:	40

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