Volume 5, Issue 2, April 2019, Page: 18-26
Amendments of a Stochastic Restricted Principal Components Regression Estimator in the Linear Model
Alaa Ahmed Abd Elmegaly, Statistics, Ministry of Higher Education and Scientific Research, Cairo, Egypt
Received: Jan. 1, 2019;       Accepted: Jun. 3, 2019;       Published: Jun. 12, 2019
DOI: 10.11648/j.ijdsa.20190502.12      View  133      Downloads  31
Abstract
Principal component Analysis (PCA) is one of the popular methods used to solve the multicollinearity problem. Researchers in 2014 proposed an estimator to solve this problem in the linear model when there were stochastic linear restrictions on the regression coefficients. This estimator was called the stochastic restricted principal components (SRPC) regression estimator. The estimator was constructed by combining the ordinary mixed estimator (OME) and the principal components regression (PCR) estimator. It ignores the number of components (orthogonal matrix Tr) that the researchers choose to solve the multicollinearity problem in the data matrix (X). This paper proposed four different methods (Lagrange function, the same technique, the constrained principal component model, and substitute in model) to modify the (SRPC) estimator to be used in case of multicollinearity. Finally, a numerical example, an application, and simulation study have been introduced to illustrate the performance of the proposed estimator.
Keywords
Constrained Principal Components Analysis, General Linear Model, Principal Component Analysis, Simulation and Application, Stochastic Restricted Principal Components
To cite this article
Alaa Ahmed Abd Elmegaly, Amendments of a Stochastic Restricted Principal Components Regression Estimator in the Linear Model, International Journal of Data Science and Analysis. Vol. 5, No. 2, 2019, pp. 18-26. doi: 10.11648/j.ijdsa.20190502.12
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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