Application of stochastic restricted least trimmed squares ridge regression in water consumption modeling
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.
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