Full bandwidth matrix selectors for gradient kernel density estimate

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F13%3A00067353" target="_blank" >RIV/00216224:14310/13:00067353 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60162694:G42__/13:00477756

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.csda.2012.07.006" target="_blank" >http://dx.doi.org/10.1016/j.csda.2012.07.006</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.csda.2012.07.006" target="_blank" >10.1016/j.csda.2012.07.006</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Full bandwidth matrix selectors for gradient kernel density estimate

Popis výsledku v původním jazyce

The most important factor in a multivariate kernel density estimation is a~choice of a bandwidth matrix. Because of its role in controlling both the amount and the direction of multivariate smoothing, this choice is a particularly important. Considerableattention has been paid to constrained parameterization of the bandwidth matrix such as a diagonal matrix or pre-transformation of the data. General multivariate kernel density derivative estimators has been investigated in paper Chac'on, Test, p. 375--398, Vol. 19, 2011. The present paper is focused on data-driven selectors of full bandwidth matrices for a density and its gradient. This method is based on an optimally balanced relation between integrated variance and integrated squared bias. The analysis of statistical properties shows the rationale of the proposed method. It is also given the relative rate of convergence to compare the method with cross-validation and plug-in methods.

Název v anglickém jazyce

Full bandwidth matrix selectors for gradient kernel density estimate

Popis výsledku anglicky

The most important factor in a multivariate kernel density estimation is a~choice of a bandwidth matrix. Because of its role in controlling both the amount and the direction of multivariate smoothing, this choice is a particularly important. Considerableattention has been paid to constrained parameterization of the bandwidth matrix such as a diagonal matrix or pre-transformation of the data. General multivariate kernel density derivative estimators has been investigated in paper Chac'on, Test, p. 375--398, Vol. 19, 2011. The present paper is focused on data-driven selectors of full bandwidth matrices for a density and its gradient. This method is based on an optimally balanced relation between integrated variance and integrated squared bias. The analysis of statistical properties shows the rationale of the proposed method. It is also given the relative rate of convergence to compare the method with cross-validation and plug-in methods.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BA - Obecná matematika

OECD FORD obor

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Projekt

<a href="/cs/project/LC06024" target="_blank" >LC06024: Centrum Jaroslava Hájka pro teoretickou a aplikovanou statistiku</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2013

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Computational Statistics & Data Analysis

ISSN

0167-9473

e-ISSN

—

Svazek periodika

57

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

13

Strana od-do

364-376

Kód UT WoS článku

000310403700027

EID výsledku v databázi Scopus

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