Department seminar by Marie-Pier Cote, Laval UniversityExport this event to calendar

Friday, March 29, 2019 — 10:30 AM EDT

Background risk model and inference based on ranks of residuals


It is often easier to model the behaviour of a random vector by choosing the marginal distributions and the copula separately rather than using a classical multivariate distribution. Many copula families, including the classes of Archimedean and elliptical copulas, may be written as the survival copula of a random vector R(X,Y), where R is a strictly positive random variable independent of the random vector (X,Y). A unified framework is presented for studying the dependence structure underlying this stochastic representation, which is called the background risk model. However, in many applications, part of the dependence may be explained by observable external factors, which justifies the use of generalized linear models for the marginal distributions. In this case and under some conditions that will be discussed, the inference on the copula can be based on the ranks of suitable residuals.

Location 
M3 - Mathematics 3
Room: 3127
200 University Avenue West

Waterloo, ON N2L 3G1
Canada

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