標題: | Multivariate normal distribution approaches for dependently truncated data |
作者: | Emura, Takeshi Konno, Yoshihiko 統計學研究所 Institute of Statistics |
關鍵字: | Correlation coefficient;Truncation;Maximum likelihood;Missing data;Multivariate analysis;Parametric bootstrap |
公開日期: | 1-二月-2012 |
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. |
URI: | http://dx.doi.org/10.1007/s00362-010-0321-x http://hdl.handle.net/11536/15244 |
ISSN: | 0932-5026 |
DOI: | 10.1007/s00362-010-0321-x |
期刊: | STATISTICAL PAPERS |
Volume: | 53 |
Issue: | 1 |
起始頁: | 133 |
結束頁: | 149 |
顯示於類別: | 期刊論文 |