Binary particle swarm optimization as a detection tool for influential subsets in linear regression

Deliorman G., Inan D.

JOURNAL OF APPLIED STATISTICS, cilt.48, sa.13-15, ss.2441-2456, 2021 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 48 Sayı: 13-15
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1080/02664763.2020.1779196
  • Dergi Adı: JOURNAL OF APPLIED STATISTICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, ABI/INFORM, Aerospace Database, Business Source Elite, Business Source Premier, CAB Abstracts, Veterinary Science Database, zbMATH
  • Sayfa Sayıları: ss.2441-2456
  • Anahtar Kelimeler: Influential subsets, binary particle swarm optimization, heuristic algorithms, linear regression, diagnostics, ALGORITHM
  • Marmara Üniversitesi Adresli: Evet

Özet

An influential observation is any point that has a huge effect on the coefficients of a regression line fitting the data. The presence of such observations in the data set reduces the sensitivity and validity of the statistical analysis. In the literature there are many methods used for identifying influential observations. However, many of those methods are highly influenced by masking and swamping effects and require distributional assumptions. Especially in the presence of influential subsets most of these methods are insufficient to detect these observations. This study aims to develop a new diagnostic tool for identifying influential observations using the meta-heuristic binary particle swarm optimization algorithm. This proposed approach does not require any distributional assumptions and also not affected by masking and swamping effects as the known methods. The performance of the proposed method is analyzed via simulations and real data set applications.