Multivariate normal distribution approaches for dependently truncated data

Takeshi Emura and Yoshihiko Konno ()

Statistical Papers, 2012, vol. 53, issue 1, 133-149

Abstract: Many statistical methods for truncated data rely on the independence assumption regarding the truncation variable. In many application studies, however, the dependence between a variable X of interest and its truncation variable L plays a fundamental role in modeling data structure. For truncated data, typical interest is in estimating the marginal distributions of (L, X) and often in examining the degree of the dependence between X and L. To relax the independence assumption, we present a method of fitting a parametric model on (L, X), which can easily incorporate the dependence structure on the truncation mechanisms. Focusing on a specific example for the bivariate normal distribution, the score equations and Fisher information matrix are provided. A robust procedure based on the bivariate t-distribution is also considered. Simulations are performed to examine finite-sample performances of the proposed method. Extension of the proposed method to doubly truncated data is briefly discussed. Copyright Springer-Verlag 2012

Keywords: Correlation coefficient; Truncation; Maximum likelihood; Missing data; Multivariate analysis; Parametric bootstrap; 62F10; 62H12; 62N01; 62N02 (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (9)

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DOI: 10.1007/s00362-010-0321-x

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