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Andishe 2022, 26(2): 9-19 Back to browse issues page
Application of stochastic restricted least trimmed squares ridge regression in water consumption modeling
Mahdi Roozbeh *, ‎Mlihe Malekjafarian, Monireh Maanavi
Semnan University
Abstract:   (564 Views)

‎The most important goal of statistical science is to analyze the real data of the world around us‎. ‎If this information is analyzed accurately and correctly‎, ‎the results will help us in many important decisions‎. ‎Among the real data around us which its analysis is very important‎, ‎is the water consumption data‎. ‎Considering that Iran is located in a semi-arid climate area of the earth‎, ‎it is necessary to take big steps for predicting and selecting the best and the most appropriate accurate models of water consumption‎, ‎which is necessary for the macro-national decisions‎. ‎But analyzing the real data is usually complicated‎. ‎In the analysis of the real data set‎, ‎we usually encounter with the problems of multicollinearity and outliers points‎. ‎Robust methods are used for analyzing the datasets with outliers and ridge method is used for analyzing the data sets with multicollinearity‎. ‎Also‎, ‎the restriction on the models is resulted from using non-sample information in estimation of regression coefficients‎. ‎In this paper‎, ‎it is proceeded to model the water consumption data using robust stochastic restricted ridge approach and then‎, ‎the performance of the proposed method is examined through a Monte Carlo simulation study‎.

Keywords: ‎Multicollinearity‎, ‎Outliers‎, ‎Ridge least trimmed squares method‎, ‎Stochastic linear restriction‎, ‎Water consumption‎.
Full-Text [PDF 259 kb]   (299 Downloads)    
Type of Study: Applicable | Subject: Special
Received: 2021/05/21 | Accepted: 2022/03/30 | Published: 2022/09/8
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Roozbeh M, Malekjafarian ‎, Maanavi M. Application of stochastic restricted least trimmed squares ridge regression in water consumption modeling. Andishe 2022; 26 (2) :9-19
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