On Kendall's regression
29.03.2019 11:15 – 12:15
RESEARCH CENTER FOR STATISTICS SEMINAR / ABSTRACT
"Conditional Kendall's tau is a measure of dependence between two random variables, conditionally on some covariates. We assume a regression-type relationship between conditional Kendall's tau and some covariates, in a semi-parametric setting with a large number of transformations of a small number of regressors. This model may be sparse, and the underlying parameter is estimated through a penalized criterion. We prove non-asymptotic bounds with explicit constants that hold with high probabilities. We derive the consistency of a two-step estimator, its asymptotic law and some oracle properties.
We show how the problem of estimating conditional Kendall's tau can be rewritten as a classification task. We detail specific algorithms adapting usual machine learning techniques, including nearest neighbors, decision trees, random forests and neural networks, to the setting of the estimation of conditional Kendall's tau. Finite sample properties of these estimators and their sensitivities to each component of the data-generating process are assessed in a simulation study. Finally, we apply all these estimators to a dataset of European stock indices."
Lieu
Bâtiment: Uni Mail
Bd du Pont-d'Arve 40
1205 Geneva
Room: M 5220, 5th floor
Organisé par
Faculté d'économie et de managementResearch Center for Statistics
Intervenant-e-s
Jean-David FERMANIAN, Professor of Finance and Statistics at ENSAE, Franceentrée libre
Classement
Catégorie: Séminaire
Mots clés: Conditional dependence measures, Kernel smoothing, Regression-type models, Conditional Kendall’stau