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A new robust regression method based on particle swarm optimization

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<mark>Journal publication date</mark>19/03/2015
<mark>Journal</mark>Communications in Statistics - Theory and Methods
Issue number6
Volume44
Number of pages11
Pages (from-to)1270-1280
Publication StatusPublished
<mark>Original language</mark>English

Abstract

Regression analysis is one of methods widely used in prediction problems. Although there are many methods used for parameter estimation in regression analysis, ordinary least squares (OLS) technique is the most commonly used one among them. However, this technique is highly sensitive to outlier observation. Therefore, in literature, robust techniques are suggested when data set includes outlier observation. Besides, in prediction a problem, using the techniques that reduce the effectiveness of outlier and using the median as a target function rather than an error mean will be more successful in modeling these kinds of data. In this study, a new parameter estimation method using the median of absolute rate obtained by division of the difference between observation values and predicted values by the observation value and based on particle swarm optimization was proposed. The performance of the proposed method was evaluated with a simulation study by comparing it with OLS and some other robust methods in the literature.