Multivariate and functional covariates and conditional copulas

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F12%3A10125963" target="_blank" >RIV/00216208:11320/12:10125963 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1214/12-EJS712" target="_blank" >http://dx.doi.org/10.1214/12-EJS712</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1214/12-EJS712" target="_blank" >10.1214/12-EJS712</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Multivariate and functional covariates and conditional copulas

Popis výsledku v původním jazyce

In this paper the interest is to estimate the dependence between two variables conditionally upon a covariate, through copula modelling. In recent literature nonparametric estimators for conditional copula functions in case of a univariate covariate havebeen proposed. The aim of this paper is to nonparametrically estimate a conditional copula when the covariate takes on values in more complex spaces. We consider multivariate covariates and functional covariates. We establish weak convergence, and biasand variance properties of the proposed nonparametric estimators. We also briefly discuss nonparametric estimation of conditional association measures such as a conditional Kendalls tau. The case of functional covariates is of particular interest and challenge, both from theoretical as well as practical point of view. For this setting we provide an illustration with a real data example in which the covariates are spectral curves. A simulation study investigating the finite-sample perform

Název v anglickém jazyce

Multivariate and functional covariates and conditional copulas

Popis výsledku anglicky

In this paper the interest is to estimate the dependence between two variables conditionally upon a covariate, through copula modelling. In recent literature nonparametric estimators for conditional copula functions in case of a univariate covariate havebeen proposed. The aim of this paper is to nonparametrically estimate a conditional copula when the covariate takes on values in more complex spaces. We consider multivariate covariates and functional covariates. We establish weak convergence, and biasand variance properties of the proposed nonparametric estimators. We also briefly discuss nonparametric estimation of conditional association measures such as a conditional Kendalls tau. The case of functional covariates is of particular interest and challenge, both from theoretical as well as practical point of view. For this setting we provide an illustration with a real data example in which the covariates are spectral curves. A simulation study investigating the finite-sample perform

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/GPP201%2F11%2FP290" target="_blank" >GPP201/11/P290: Metody statistické inference založené na matici párových vzdáleností</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název periodika

Electronic Journal of Statistics

ISSN

1935-7524

e-ISSN

—

Svazek periodika

6

Číslo periodika v rámci svazku

Neuveden

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

34

Strana od-do

1273-1306

Kód UT WoS článku

000306920500001

EID výsledku v databázi Scopus

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