Title: Multivariate normal distribution approaches for dependently truncated data
Authors: Emura, Takeshi
Konno, Yoshihiko
Institute of Statistics
Keywords: Correlation coefficient;Truncation;Maximum likelihood;Missing data;Multivariate analysis;Parametric bootstrap
Issue Date: 1-Feb-2012
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.
URI: http://dx.doi.org/10.1007/s00362-010-0321-x
ISSN: 0932-5026
DOI: 10.1007/s00362-010-0321-x
Volume: 53
Issue: 1
Begin Page: 133
End Page: 149
Appears in Collections:Articles

Files in This Item:

  1. 000299293800011.pdf