Robust Ridge-MM Estimator in Restricted Additive Partially Regression Model
DOI:
https://doi.org/10.33095/gj5k8e35Keywords:
Additive Partial Regression Model, Generalized least-squares, Restrictions, Multicollinearity, Ridge Estimators, MM- Estimators, Local Polynomial Smoother, Air Quality Index (AQI).Abstract
This paper, utilized the Restricted Partially Additive Regression Model to analyze air quality data in Baghdad governorate, with a focus on addressing multicollinearity issues among independent variables and outliers in the dependent variable. Through the implementation of classical estimators, ridge estimators, robust estimators, and the imposition of non-random constraints on the parametric parts of the model Through method of Robust Ridge-MM Estimator in Restricted Additive Partially Regression Model, the study aimed to assess the model's effectiveness in dealing with air pollution challenges during the summer season. Results obtained through the use of pre-built packages and algorithms in the R programming language indicated that integrating non-random constraints with robust estimators positively impacted the accuracy of estimating functions. Furthermore, certain variables, such as PM10 (airborne particles with an aerodynamic diameter of up to 10 micrometres), were found to have a significant impact on air quality This is through the parameter values. Non-linear effects were observed for some non-parametric variables. The study highlights the importance of understanding the effects of air pollutants on public health and emphasizes the urgent need for quick solutions to mitigate these negative effects.
Paper type: Research paper.
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