Generalized Additive Modeling For Multivariate Distributions
Details
Download: BIB_E1AD7D2DCF57.P001.pdf (6595.39 [Ko])
State: Public
Version: After imprimatur
State: Public
Version: After imprimatur
Serval ID
serval:BIB_E1AD7D2DCF57
Type
PhD thesis: a PhD thesis.
Collection
Publications
Institution
Title
Generalized Additive Modeling For Multivariate Distributions
Director(s)
Chavez-Demoulin V.
Institution details
Université de Lausanne, Faculté des hautes études commerciales
Address
Faculté des hautes études commerciales (HEC) Université de Lausanne CH-1015 Lausanne SUISSE
Publication state
Accepted
Issued date
2016
Language
english
Abstract
In this thesis, we develop tools to study the influence of predictors on multivariate distributions. We tackle the issue of conditional dependence modeling using generalized additive models, a natural extension of linear and generalized linear models allowing for smooth functions of the covariates. Compared to existing methods, the framework that we develop has two main advantages. First, it is completely flexible, in the sense that the dependence structure can vary with an arbitrary set of covariates in a parametric, nonparametric or semiparametric way. Second, it is both quick and numerically stable, which means that it is suitable for exploratory data analysis and stepwise model building. Starting from the bivariate case, we extend our framework to pair-copula constructions, and open new possibilities for further applied and methodological work. Our regression-like theory of the dependence, being built on conditional copulas and generalized additive models, is at the same time theoretically sound and practically useful.
Create date
24/05/2016 15:53
Last modification date
20/08/2019 16:05