Conditional maximum Lq-likelihood estimation for regression model with autoregressive error terms

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GÜNEY Y., Tuac Y., Ozdemir S., ARSLAN O.

Metrika, cilt.84, sa.1, ss.47-74, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 84 Sayı: 1
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1007/s00184-020-00774-2
  • Dergi Adı: Metrika
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, EconLit, zbMATH
  • Sayfa Sayıları: ss.47-74
  • Anahtar Kelimeler: Autoregressive stationary process, Conditional maximum Lq-likelihood, Linear regression, PARAMETER
  • Ankara Üniversitesi Adresli: Evet

Özet

© 2020, Springer-Verlag GmbH Germany, part of Springer Nature.In this article, we consider the parameter estimation of regression model with pth-order autoregressive (AR(p)) error term. We use the maximum Lq-likelihood (MLq) estimation method proposed by Ferrari and Yang (Ann Stat 38(2):753–783, 2010), as a robust alternative to the classical maximum likelihood (ML) estimation method to handle the outliers in the data. After exploring the MLq estimators for the parameters of interest, we provide some asymptotic properties of the resulting MLq estimators. We give a simulation study and three real data examples to illustrate the performance of the proposed estimators over the ML estimators and observe that the MLq estimators have superiority over the ML estimators when some outliers are present in the data.