Theory of the generalized least squares estimator in parametric estimation

• Main Authors: Hsing-Ti Wu, 吳行悌
• Other Authors: Fushing Hsieh
• Format: Others
• Language: zh-TW
• Published: 1993
• Online Access: http://ndltd.ncl.edu.tw/handle/70087959258290185184

碩士 === 國立清華大學 === 統計學研究所 === 81 === In parametric estimation, the maximum likelihood principle has played an important role for the recent years. Here, we introduce a different approach by transforming the model considered into a linear model, based on which,one then constructs the generalized least squares GLS estimator. It is shown that in one-sample parametric model, the GLS estimator is asymptotically equivalent to the {\em MLE}. We also show, for the Weibull case,the {\em GLS} estimator is also asymptotically equivalent to the {\em MLE}. After that, we discuss the robustness property of the GLS estimator. And then, we make a suggestion for how to choose the regression point for the transformed linear model.