Proposed Two-Parameter Estimator for Estimating Linear Regression Model and Comparing It with Some Other Estimators

NoorAlzahraa Naeem Abd Ali , Shrooq Abdul Redha Al Sabah

In this paper, a new two-parameter estimator was proposed to estimate the parameters of the linear regression model that has the ability to face the problem of Multicollinearity based on the previous information about the parameters to be estimated and this estimator was compared with the two-parameter estimator of the linear regression model of Kaciranlar and the two-parameter estimator of the linear regression model (Lokman et al. [1]) using the mean square error criterion (MSE) for each model by conducting Monte-Carlo simulation to study the behavior of the proposed estimator. It was concluded that the proposed method is better than the rest of the estimation methods because it achieved the lowest comparison criteria, and in the case of high Multicollinearity between the explanatory variables, the proposed method was very effective in solving this problem. Data representing (100) observations of the number of women with Irritable Bowel Syndrome (IBS) for the years (2020-2023) from the Karbala Holy Health Department were used, which represents the dependent variable (y) and a group of variables affecting the incidence of the disease, with nineteen variables. It was concluded that irritable bowel syndrome among women is decreasing, as the predictive values ​​according to the proposed method are appropriate for the estimated values ​​during the next five years.

Doi: https://doi.org/10.54216/PMTCS.040201

Vol. 4 Issue. 2 PP. 01-22, (2024)