A New Robust Regression Method Based on Particle Swarm Optimization


Cagcag Ö., YOLCU U., Egrioglu E.

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, cilt.44, sa.6, ss.1270-1280, 2015 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 44 Sayı: 6
  • Basım Tarihi: 2015
  • Doi Numarası: 10.1080/03610926.2012.718843
  • Dergi Adı: COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1270-1280
  • Anahtar Kelimeler: Linear model, Particle swarm optimization, Robust regression estimator, Simulation
  • Marmara Üniversitesi Adresli: Hayır

Özet

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.