Conditionalization of Copula-Based Models

Authors:
Dorota Kurowicka
Affiliations:
Delft Institute of Applied Mathematics, Delft University of Technology, 2628 CD Delft, The Netherlands
Venue:
Decision Analysis
Year:
2012

Citing 2
Cited 2

Elements of information theory

Elements of information theory
An Introduction to Copulas (Springer Series in Statistics)

An Introduction to Copulas (Springer Series in Statistics)

From the Editors---Copulas, Group Preferences, Multilevel Defenders, Sharing Rewards, and Communicating Analytics

Decision Analysis
From the Editors---Brainstorming, Multiplicative Utilities, Partial Information on Probabilities or Outcomes, and Regulatory Focus

Decision Analysis

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Abstract

Copula models are becoming increasingly popular in engineering and financial applications. They provide a flexible way of constructing joint distributions with arbitrary one-dimensional margins and a wide variety of dependence structures. This paper studies different types of conditionalization of copula-based models. We conditionalize these models in the usual way on point values but also propose a different type of conditionalization on a new margin. This new type of conditionalization is motivated by experience with a model built for application in modeling risk in civil aviation. Changing one margin in a copula model is very easy; however, it is not equivalent to conditionalizing on a new margin. For this purpose, the technique presented by Holland and Wang [Holland PW, Wang YJ (1987) Dependence function for continuous bivariate densities. Commun. Statist. Theory Methods 16(3):867--876], called marginal replacement, is used. The main result of this paper allows a simplified way of conditionalizing on more than one univariate margin in a normal copula model.