Distribution-free and link-free estimation for a multivariate semiparametric sample selection model
CHAVENT, Marie
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
SARACCO, Jérôme
Quality control and dynamic reliability [CQFD]
Groupe de Recherche en Economie Théorique et Appliquée [GREThA]
Quality control and dynamic reliability [CQFD]
Groupe de Recherche en Economie Théorique et Appliquée [GREThA]
CHAVENT, Marie
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
SARACCO, Jérôme
Quality control and dynamic reliability [CQFD]
Groupe de Recherche en Economie Théorique et Appliquée [GREThA]
< Réduire
Quality control and dynamic reliability [CQFD]
Groupe de Recherche en Economie Théorique et Appliquée [GREThA]
Langue
en
Communication dans un congrès
Ce document a été publié dans
Proceedings of the XIIth International Symposium of Applied Stochastic Models and Data Analysis, Proceedings of the XIIth International Symposium of Applied Stochastic Models and Data Analysis, ASMDA 2007, 2007-05, Chania, Crète. 2007-05p. -
Résumé en anglais
Most of the prevalent estimation methods for sample selection model rely heavely on parametric assumptions. We consider in this communication a multivariate semiparametric sample selection model and we develop a geometric ...Lire la suite >
Most of the prevalent estimation methods for sample selection model rely heavely on parametric assumptions. We consider in this communication a multivariate semiparametric sample selection model and we develop a geometric approach to the estimation of the slope vectors in the outcome equation and in the selection equation. Contrary to most existing methods, we deal symmetrically with both slope vectors. The estimation method is link-free and distribution-free, it works in two main steps: a multivariate Sliced Inverse Regression step, and a Canonical Analysis step. We establish √n-consistency and asymptotic normality of the estimates. We give results from a simulation study in order to illustrate the estimation method.< Réduire
Mots clés en italien
Multivariate Sliced Inverse Regression
Canonical Analysis
Semipara- metric Regression Models
Origine
Importé de halUnités de recherche