Authors:
Omar Abdulmohsin Ali,Mohammed Ahmed Abbas,DOI NO:
https://doi.org/10.26782/jmcms.2019.12.00027Keywords:
Segmented regression,change point,rank-based estimator,iterative reweighted least squares,M-estimator,Abstract
Segmentation has vital employment in regression analysis where data have some change point. Traditional estimation methods such as Hudson, D.J.;(1966) and Muggeo, V. M., (2003)have been reviewed. But these methods do not take into account robustness in the presence of outliers values. However, third method was used as rank-based method, where the analysis will be devoted to the ranks of data instead of the data themselves. Our contribution in this paper is to use M-estimator methodology with three distinct weight functions (Huber, Tukey, and Hampel) which has been combined with Muggeo version approach to gain more robustness, Thus we get robust estimates from the change point and regression parameters simultaneously. We call our new estimator as robust Iterative Rewrighted Mestimator: IRWm-method with respect to its own weight function. Our primary interest is to estimate the change point that joins the segments of regression curve, and our secondary interest is to estimate the parameters of segmented regression model. The real data set were used which concerned to bed-loaded transport as dependent variable (y) and discharge explanatory variable (x). The comparison has been conducted by using several criteria to select the most appropriate method for estimating the change point and the regression parameters. The superior results were marked for IRWm-estimator with respect to Tukey weight function.Refference:
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