Author : Wei Tu
Publisher :
ISBN 13 :
Total Pages : 73 pages
Book Rating : 4.:/5 (959 download)
Book Synopsis Robust Adaptively Weighted Estimators for Regression Models by : Wei Tu
Download or read book Robust Adaptively Weighted Estimators for Regression Models written by Wei Tu and published by . This book was released on 2015 with total page 73 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis introduces a new class of robust estimators for regression models. Specifically, a class of weighted least square estimators under linear regression models is introduced in Chapter 2, with a continuous adaptive weight function computed using the Kolmogorov-Smirnov statistic. Asymptotic properties, such as consistency and asymptotic normality, of the proposed estimator are established under the model. Simulation studies show that the proposed estimator attains almost full efficiency and have a better robustness properties than the initial estimators for finite sample sizes. An application to a real contaminated dataset shows that it's comparable to other robust estimators in practice. In Chapter 3, a class of weighted maximum likelihood estimators under logistic regression models is introduced, again with a continuous adaptive weight function computed using Mahalanobis distances of exploratory variables. Asymptotic consistency of the proposed estimator is proved under the model, and finite-sample properties are also studied by simulation. In simulation studies, it is observed that the proposed estimator is almost as efficient as the maximum likelihood estimator under the model, and under point-mass contamination models, the proposed estimator shows a comparable robustness. This is also verified in an application to a real data set. Chapter 4 contains some concluding remarks and future directions.
