Generalized inference in heteroscedastic multivariate linear models

• Main Author: 王仁聖
• Other Authors: 洪慧念
• Format: Others
• Language: en_US
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/90735848617742765093

博士 === 國立交通大學 === 統計學研究所 === 96 === Our main subject in this dissertation is applying the generalized method to deal with regression model with heteroscedastic AR(1) covariance matrices. The concepts of the generalized p-values and the generalized confidence intervals proposed by Tsui and Weerahandi (1989) and Weerahandi (1993), respectively, provide an alternative way to handle with heteroscedasticity. We extend these concepts to further consider the standardized expression of the generalized multivariate test variable. Lin and Lee (2003) applied the generalized method to deal with the MANOVA model with unequal uniform covariance structures among multiple groups. We utilize their process with modifications to deal with regression model with heteroscedastic serial dependence. The coverage probabilities and expected areas based on our proposed procedure display satisfactory results. Besides, we also find that our method can be applied to the uniform structures without the special design matrices assumption.