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자연과학대학
통계학전공
Journal papers
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A copula transformation in multivariate mixed discrete-continuous models
Title
A copula transformation in multivariate mixed discrete-continuous models
Authors
Ahn J.Y.
;
Fuchs S.
;
Oh R.
Ewha Authors
안재윤
SCOPUS Author ID
안재윤
Issue Date
2021
Journal Title
Fuzzy Sets and Systems
ISSN
0165-0114
Citation
Fuzzy Sets and Systems vol. 415, pp. 54 - 75
Keywords
Collective risk model
;
Copula density function
;
Copula transformation
;
Mixed discrete-continuous variable
Publisher
Elsevier B.V.
Indexed
SCIE; SCOPUS
Document Type
Article
Abstract
Copulas allow a flexible and simultaneous modeling of complicated dependence structures together with various marginal distributions. Especially if the density function can be represented as the product of the marginal density functions and the copula density function, this leads to both an intuitive interpretation of the conditional distribution and convenient estimation procedures. However, this is no longer the case for copula models with mixed discrete and continuous marginal distributions, because the corresponding density function cannot be decomposed so nicely. In this paper, we introduce a copula transformation method that allows to represent the density function of a distribution with mixed discrete and continuous marginals as the product of the marginal probability mass/density functions and the copula density function. With the proposed method, conditional distributions can be described analytically and the computational complexity in the estimation procedure can be reduced depending on the type of copula used. © 2020 Elsevier B.V.
DOI
10.1016/j.fss.2020.11.008
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