Visualization and Bandwidth Matrix Choice

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F12%3A00059046" target="_blank" >RIV/00216224:14310/12:00059046 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60162694:G42__/12:00477752

Výsledek na webu

<a href="http://dx.doi.org/10.1080/03610926.2010.529539" target="_blank" >http://dx.doi.org/10.1080/03610926.2010.529539</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/03610926.2010.529539" target="_blank" >10.1080/03610926.2010.529539</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Visualization and Bandwidth Matrix Choice

Popis výsledku v původním jazyce

Kernel smoothers are among the most popular nonparametric functional estimates. These estimates depend on a bandwidth which controls the smoothness of the estimate. While the literature for a bandwidth choice in a univariate density estimate is quite extensive, the progress in the multivariate case is slower. We focus on a bandwidth matrix selection for a bivariate kernel density estimate provided that the bandwidth matrix is diagonal. A common task is to find entries of the bandwidth matrix which minimizes the Mean Integrated Square Error (MISE). It is known that in this case there exists explicit solution of an asymptotic approximation of MISE (Wand and Jones, 1995). In the present paper we pay attention to the visualization and optimizers are presented as intersection of bivariate functional surfaces derived from this explicit solution and we develop the method based on this visualization. A simulation study compares the least square cross-validation method and the proposed method.

Název v anglickém jazyce

Visualization and Bandwidth Matrix Choice

Popis výsledku anglicky

Kernel smoothers are among the most popular nonparametric functional estimates. These estimates depend on a bandwidth which controls the smoothness of the estimate. While the literature for a bandwidth choice in a univariate density estimate is quite extensive, the progress in the multivariate case is slower. We focus on a bandwidth matrix selection for a bivariate kernel density estimate provided that the bandwidth matrix is diagonal. A common task is to find entries of the bandwidth matrix which minimizes the Mean Integrated Square Error (MISE). It is known that in this case there exists explicit solution of an asymptotic approximation of MISE (Wand and Jones, 1995). In the present paper we pay attention to the visualization and optimizers are presented as intersection of bivariate functional surfaces derived from this explicit solution and we develop the method based on this visualization. A simulation study compares the least square cross-validation method and the proposed method.

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)

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název periodika

Communications in Statistics - Theory and Methods

ISSN

0361-0926

e-ISSN

—

Svazek periodika

41

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

19

Strana od-do

759-777

Kód UT WoS článku

000304523600014

EID výsledku v databázi Scopus

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