|標題:||Multivariate normal distribution approaches for dependently truncated data|
Institute of Statistics
|關鍵字:||Correlation coefficient;Truncation;Maximum likelihood;Missing data;Multivariate analysis;Parametric bootstrap|
|摘要:||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.|
|Appears in Collections:||Articles|
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