Parallel maximum likelihood estimator for multiple linear regression models

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

卷:273

Article

Guo, Guangbao^1,2  You, Wenjie³  Qian, Guoqi⁴  Shao, Wei⁵ 

[1] Shandong Univ Technol, Dept Stat, Zibo 255000, Peoples R China

[2] Shandong Univ, Sch Math, Jinan 250100, Peoples R China

[3] Xiamen Univ, Dept Automat, Xiamen 361005, Peoples R China

[4] Univ Melbourne, Dept Math & Stat, Parkville, Vic 3010, Australia

[5] Qufu Normal Univ, Sch Management, Rizhao 276800, Peoples R China

关键词: Multiple linear regression models;    Parallel computing;    Maximum likelihood estimator;    Consistency;    Outlier;   

DOI  :  10.1016/j.cam.2014.06.005

来源: Elsevier

【 摘 要 】

Consistency and run-time are important questions in performing multiple linear regression models. In response, we introduce a new parallel maximum likelihood estimator for multiple linear models. We first provide an equivalent condition between the method and the generalized least squares estimator. We also consider the rank of projections and the eigenvalue. We then present consistency when a stable solution exists. In this paper, we describe several consistency theorems and perform experiments on consistency, outlier, and scalability. Finally, we fit the proposed method onto bankruptcy data. (C) 2014 Elsevier B.V. All rights reserved.

【 授权许可】

Free