Comparison of Some Estimator Methods of Regression Mixed Model for the Multilinearity Problem and High – Dimensional Data

https://doi.org/10.55529/jecnam.33.1.11

Authors

  • Thaer Hashim Abdul Muttaleb College of Medicine/ Wasit University/ Family and Community Medicine Branch, Iraq

Keywords:

Mixed Model, Lasso Regression, Bayesian Ridge.

Abstract

In order to obtain a mixed model with high significance and accurate alertness, it is necessary to search for the method that performs the task of selecting the most important variables to be included in the model, especially when the data under study suffers from the problem of multicollinearity as well as the problem of high dimensions. The research aims to compare some methods of choosing the explanatory variables and the estimation of the parameters of the regression model, which are Bayesian Ridge Regression (unbiased) and the adaptive Lasso regression model, using simulation. MSE was used to compare the methods.

Published

2023-04-03

How to Cite

Thaer Hashim Abdul Muttaleb. (2023). Comparison of Some Estimator Methods of Regression Mixed Model for the Multilinearity Problem and High – Dimensional Data. Journal of Electronics, Computer Networking and Applied Mathematics , 3(03), 1–11. https://doi.org/10.55529/jecnam.33.1.11

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