Rank-based inference tools for copula regression, with property and casualty insurance applications

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F19%3A10402069" target="_blank" >RIV/00216208:11320/19:10402069 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=S7su54h2xN" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=S7su54h2xN</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.insmatheco.2019.08.001" target="_blank" >10.1016/j.insmatheco.2019.08.001</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Rank-based inference tools for copula regression, with property and casualty insurance applications

Popis výsledku v původním jazyce

Rank-based procedures are commonly used for inference in copula models for continuous responses whose behavior does not depend on covariates. This paper describes how these procedures can be adapted to the broader framework in which (possibly non-linear) regression models for the marginal responses are linked by a copula that does not depend on covariates. The validity of many of these techniques can be derived from the asymptotic equivalence between the classical empirical copula process and its analog based on suitable residuals from the marginal models. Moment-based parameter estimation and copula goodness-of-fit tests are shown to remain valid under weak conditions on the marginal error term distributions, even when the residual-based empirical copula process fails to converge weakly. The performance of these procedures is evaluated through simulation in the context of two general insurance applications: micro-level multivariate insurance claims, and dependent loss triangles. (C) 2019 The Authors. Published by Elsevier B.V.

Název v anglickém jazyce

Rank-based inference tools for copula regression, with property and casualty insurance applications

Popis výsledku anglicky

Rank-based procedures are commonly used for inference in copula models for continuous responses whose behavior does not depend on covariates. This paper describes how these procedures can be adapted to the broader framework in which (possibly non-linear) regression models for the marginal responses are linked by a copula that does not depend on covariates. The validity of many of these techniques can be derived from the asymptotic equivalence between the classical empirical copula process and its analog based on suitable residuals from the marginal models. Moment-based parameter estimation and copula goodness-of-fit tests are shown to remain valid under weak conditions on the marginal error term distributions, even when the residual-based empirical copula process fails to converge weakly. The performance of these procedures is evaluated through simulation in the context of two general insurance applications: micro-level multivariate insurance claims, and dependent loss triangles. (C) 2019 The Authors. Published by Elsevier B.V.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GA19-00015S" target="_blank" >GA19-00015S: Identifikace schémat časového vývoje indikátorů chudoby a sociálního vyčlenění domácností založená na vícerozměrných panelových datech smíšeného typu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

Insurance: Mathematics and Economics

ISSN

0167-6687

e-ISSN

—

Svazek periodika

89

Číslo periodika v rámci svazku

November

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

15

Strana od-do

1-15

Kód UT WoS článku

000501619400001

EID výsledku v databázi Scopus

2-s2.0-85072186873