A semiparametric approach for a multivariate sample selection model

Chavent, Marie, Liquet, Benoit and Saracco, Jerome (2010) A semiparametric approach for a multivariate sample selection model. Statistica Sinica, 20 2: 513-536.

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Author Chavent, Marie
Liquet, Benoit
Saracco, Jerome
Title A semiparametric approach for a multivariate sample selection model
Journal name Statistica Sinica   Check publisher's open access policy
ISSN 1017-0405
Publication date 2010-04
Year available 2010
Sub-type Article (original research)
Open Access Status File (Publisher version)
Volume 20
Issue 2
Start page 513
End page 536
Total pages 24
Place of publication Taiwan, Republic of China
Publisher Academia Sinica, Institute of Statistical Science
Collection year 2011
Language eng
Formatted abstract
Most of the common estimation methods for sample selection models rely heavily on parametric and normality assumptions. We consider in this paper a multivariate semiparametric sample selection model and 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. Moreover, 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 describe how to estimate the observation and selection link functions. The theory is illustrated with a simulation study.
Keyword Canonical analysis
Eigen decomposition
multivariate SIR
Semiparametric regression models
Sliced Inverse Regression (SIR)
Dimension Reduction
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Mathematics and Physics
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Created: Fri, 13 Sep 2013, 16:11:52 EST by Kay Mackie on behalf of School of Mathematics & Physics