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 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 penalized log-likelihood estimator and derive its root-n-consistency and asymptotic normality. Finally, we present the results from a simulation study and apply the new methodology to a real dataset.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 penalized log-likelihood estimator and derive its root-n-consistency and asymptotic normality. Finally, we present the results from a simulation study and apply the new methodology to a real dataset.