A multi-objective programming approach to Weibull parameter estimation

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KOÇAK E., DEMİR YURTSEVEN E., ÖRKCÜ H. H.

HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS, cilt.51, sa.2, ss.543-558, 2022 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 51 Sayı: 2
  • Basım Tarihi: 2022
  • Doi Numarası: 10.15672/hujms.912435
  • Dergi Adı: HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, zbMATH, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.543-558
  • Anahtar Kelimeler: Weibull parameter estimation, multi-objective programming, NSGA-II, PARTICLE SWARM OPTIMIZATION, LEAST-SQUARES ESTIMATION
  • Sivas Cumhuriyet Üniversitesi Adresli: Evet

Özet

Weibull distribution is widely used in various areas such as life tables, failure rates, and definition of wind speed distribution. Therefore, parameter estimation for the Weibull distribution is an important problem in many real data applications. The least square (LS), the weighted least square (WLS) and the maximum likelihood (ML) are the most popular methods for the parameter estimation in the Weibull distribution. In this study, based on the LS, WLS and ML estimation methods, a multi-objective programming approach is proposed for the parameter estimation of two-parameter Weibull distribution. This new approach evaluates together LS, WLS and ML methods in the estimation process. NSGA-II method, which is a multi-objective heuristic optimization method, is used to solve the proposed multi-objective estimation model. To evaluate the applicability and performance of the proposed approach, a detailed Monte Carlo simulation study based on deficiency criteria and a real data application are designed. The results illustrated that the proposed multi-objective programming approach provides quite accurate parameter estimates for the two parameter Weibull distribution with respect to deficiency criteria. Mathematics Subject Classification (2020). 58E17, 62F07, 62F10