Robust change point estimation in two-phase linear regression models: An application to metabolic pathway data

Acitas S., ŞENOĞLU B.

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, cilt.363, ss.337-349, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 363
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1016/j.cam.2019.06.020
  • Dergi Adı: JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, MathSciNet, Metadex, zbMATH, DIALNET, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.337-349
  • Anahtar Kelimeler: Change point, Efficiency, Modified maximum likelihood, Regression, Robustness, MAXIMUM-LIKELIHOOD, ASYMPTOTICS, PARAMETERS, DISTRIBUTIONS, DEVIATION, LOCATION
  • Ankara Üniversitesi Adresli: Evet

Özet

In this study, we develop robust versions of the change point estimation methods given by Hudson (1966) and Muggeo (2003) in the two-phase linear regression model. We use a modified maximum likelihood (MML) methodology originated by Tiku (1967, 1968) when the error terms of a two-phase linear regression model are independently and identically distributed as long-tailed symmetric. Proposed estimators are shown to be more efficient and robust using the Monte-Carlo simulation. Julious's (Julious, 2001) metabolic pathway data is analyzed in the application part of the study. It is shown that for this data using a LS estimator is inappropriate since there is an outlying observation. We therefore use proposed robust estimators instead of LS estimators and obtain more reliable results. (C) 2019 Elsevier B.V. All rights reserved.