Parallel maximum likelihood estimator for multiple linear regression models

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.