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ESTIMATION FOR THE GENERAL SAMPLE SELECTION MODELS
Author(s) -
Wang YouGan,
Yin Ming
Publication year - 1997
Publication title -
australian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 0004-9581
DOI - 10.1111/j.1467-842x.1997.tb00519.x
Subject(s) - estimator , mathematics , selection (genetic algorithm) , observable , kernel (algebra) , statistics , variable (mathematics) , function (biology) , outcome (game theory) , sample (material) , econometrics , computer science , mathematical economics , artificial intelligence , mathematical analysis , combinatorics , physics , quantum mechanics , evolutionary biology , biology , chemistry , chromatography
summary Consider a general regression model with an arbitrary and unknown link function and a stochastic selection variable that determines whether the outcome variable is observable or missing. The paper proposes U‐statistics that are based on kernel functions as estimators for the directions of the parameter vectors in the link function and the selection equation, and shows that these estimators are consistent and asymptotically normal.