A comparative study of some robust nonlinear regression methods
Article Sidebar
August 18, 2019
May 1, 2019
Abstract
Least squares (LS) with Gauss-Newton method is the most widely used approach to estimate the parameters of nonlinear regression models. In the presence of outliers, even one single unusual value may have a large effect on the parameter estimates. This paper aims to introduce some popular robust nonlinear techniques that commonly used as a better alternative method to the classical least squares. This includes M-estimator and MM. In addition, the target is to compare their practical performance under a variety of circumstances such as sample size, percentage of outliers and model formula. Results of Monte Carlo simulations and real data example using R software, indicated that the best performance has been achieved by MM followed by M estimator for all possible percentages of outliers (10%, 20%, 30%, 40%) as well as all sample sizes (n=50, n=100, and n=150). Moreover, results approved that the LS estimator remains the best when there is no outlier in data.
Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
In a brief statement, the rights relate to the publication and distribution of research published in the journal of the University of Sebha where authors who have published their articles in the journal of the university of Sebha should how they can use or distribute their articles. They reserve all their rights to the published works, such as (but not limited to) the following rights:
- Copyright and other property rights related to the article, such as patent rights.
- Research published in the journal of the University of Sebha and used in its future works, including lectures and books, the right to reproduce articles for their own purposes, and the right to self-archive their articles.
- The right to enter a separate article, or for a non-exclusive distribution of their article with an acknowledgment of its initial publication in the journal of Sebha University.
Privacy Statement The names and e-mail addresses entered on the Sabha University Journal site will be used for the aforementioned purposes only and for which they were used.
