Generalized additive models for conditional dependence structures

Generalized additive models for conditional dependence structures

We develop a generalized additive modeling framework for taking into account the effect of predictors on the dependence structure between two variables. We consider dependence or concordance measures that are solely functions of the copula, because they contain no marginal information: rank correlation coefficients or tail-dependence coefficients represent natural choices. We propose a maximum ...

DIPARTIMENTO DI SCIENZE STATISTICHE Seminar GENERALIZED ADDITIVE MODELS FOR CONDITIONAL DEPENDENCE STRUCTURES

www.stat.unipd.it/fare-ricerca/seminari DIPARTIMENTO DI SCIENZE STATISTICHE UNIVERSITÀ DEGLI STUDI DI PADOVA 2 GENERALIZED ADDITIVE MODELS FOR CONDITIONAL DEPENDENCE STRUCTURES VALERIE CHAVEZ-DEMOULIN FULL PROFESSOR, DEPARTMENT OF OPERATIONS FACULTY OF BUSINESS AND ECONOMICS – UNIVERSITY OF LAUSANNE – (CH) Abstract We develop a generalized additive modeling framework for taking into account the...

Additive models for conditional copulas

Conditional copulas are flexible statistical tools that couple joint conditional and marginal conditional distributions. In a linear regression setting with more than one covariate and two dependent outcomes, we consider additive models for studying the dependence between covariates and the copula parameter. We examine the computation and model selection tools needed for Bayesian inference. The...

Functional Generalized Additive Models.

We introduce the functional generalized additive model (FGAM), a novel regression model for association studies between a scalar response and a functional predictor. We model the link-transformed mean response as the integral with respect to t of F{X(t), t} where F(·,·) is an unknown regression function and X(t) is a functional covariate. Rather than having an additive model in a finite number ...

Generalized Likelihood Ratio Tests for Additive Models