Using the hybrid proposed method for Quantile Regression and Multivariate Wavelet in estimating the Linear model Parameters
Abstract
In this paper, a hybrid method of quantile regression and multivariate wavelet is proposed to deal with the problem of data contamination or the presence of outliers, which uses the median instead of the mean on which the linear regression model and the estimation method for ordinary least squares depend. The paper included a comparison between the proposed (for several wavelets and different threshold) and classical method based on mean absolute error, to get the best fit quantile regression model for the data. The application part dealt with two types of data representing simulation, real data, and analysis using a program designed for this purpose in the MATLAB language, as well as the statistical program SPSS-26 and EasyFit-5.5. The study concluded that the proposed method is more efficient than the classical method in estimating the parameters of a quantile regression model depending on the coefficient of determination and on the mean absolute error and mean squared error criteria.
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