Pure Mathematics for Theoretical Computer Science

Journal DOI

https://doi.org/10.54216/PMTCS

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2995-3162ISSN (Online)

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.

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

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

Prediction of Tuberculosis in Iraq Using A ZIPR Model

Afraa A. Hamada

In this article, the ZeroInflated Poisson Regression model (ZI-PRM) was used to predict the number of tuberculosis patients by estimating the model using the maximum likelihood method and compared with Poisson regression model (PRM). The results showed that the ZIPRM best represented TB data from PRM. The PRM showed that the importance of some variables, although they were not significant as a cause of the TB data. The ZIP model indicates that there will be more TB cases in 2027 than there were in 2023. These findings point to an improvement in the nation's health status.

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Doi: https://doi.org/10.54216/PMTCS.040202

Vol. 4 Issue. 2 PP. 23-31, (2024)