Springer New York;Berlin/Heidelberg: Springer Science and Business Media LLC
摘要:
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. 其他題名： Stat Papers 出版者： Berlin/Heidelberg: Springer Science and Business Media LLC 出版日期： 2012-02-01 出處： Statistical Papers, 2012-02, Vol.53 (1), p.133-149 資源來源： EBSCOhost Business Source Premier 版權： Springer-Verlag 2010 版權： Springer-Verlag 2012 識別號： ISSN: 0932-5026 識別號： EISSN: 1613-9798 識別號： DOI: 10.1007/s00362-010-0321-x
