Elliptical regression models for multivariate sample-selection bias correction

• Published in: Journal of the Korean Statistical Society Vol. 45; no. 3; pp. 422 - 438
• Main Authors: Kim, Hea-Jung, Kim, Hyoung-Moon
• Format: Journal Article
• Language: English
• Published: Singapore Elsevier B.V 01.09.2016; Springer Singapore; 한국통계학회
• Subjects: Applied Statistics; Bayesian Inference; Bias correction; Heckman model; MCECM algorithm; Multivariate sample-selection regression; Rectangle-screened scale mixture of normal; Statistical Theory and Methods; Statistics; Statistics and Computing/Statistics Programs; 통계학; secondary; Bias correction; Multivariate sample-selection regression; MCECM algorithm; Heckman model; Rectangle-screened scale mixture of normal; primary; primary 62J99; secondary 60E05
• ISSN: 1226-3192; 2005-2863
• DOI: 10.1016/j.jkss.2016.01.003

In linear regression, a multivariate sample-selection scheme often applies to the dependent variable, which results in missing observations on the variable. This induces the sample-selection bias, i.e. a standard regression analysis using only the selected cases leads to biased results. To solve the bias problem, in this paper, we propose a class of multivariate selection regression models by extending classic Heckman model to allow for multivariate sample-selection scheme and robustness against departures from normality. Necessary theories for building a formal bias correction procedure, based upon the proposed model, are obtained, and an efficient estimation method for the model is provided. Simulation results and a real data example are presented to demonstrate the performance of the estimation method and practical usefulness of the multivariate sample-selection models.