Full bandwidth matrix selectors for gradient kernel density estimate

Result code in 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>

Alternative codes found

RIV/60162694:G42__/13:00477756

Result on the web

<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>

Result language

angličtina

Original language name

Full bandwidth matrix selectors for gradient kernel density estimate

Original language description

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.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

BA - General mathematics

OECD FORD branch

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Project

<a href="/en/project/LC06024" target="_blank" >LC06024: Jaroslav Hájek Center for Theoretical and Applied Statistics</a><br>

Continuities

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

Publication year

2013

Confidentiality

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

Name of the periodical

Computational Statistics & Data Analysis

ISSN

0167-9473

e-ISSN

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Volume of the periodical

57

Issue of the periodical within the volume

1

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

13

Pages from-to

364-376

UT code for WoS article

000310403700027

EID of the result in the Scopus database

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