Parameter estimation of regression model with AR(p) error terms based on skew distributions with EM algorithm

Tuac Y., GÜNEY Y., ARSLAN O.

Soft Computing, cilt.24, sa.5, ss.3309-3330, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 24 Sayı: 5
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1007/s00500-019-04089-x
  • Dergi Adı: Soft Computing
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, zbMATH
  • Sayfa Sayıları: ss.3309-3330
  • Anahtar Kelimeler: Autoregressive stationary process, EM algorithm, Linear regression, Skew distributions, AUTOREGRESSIVE MODELS, LIKELIHOOD
  • Ankara Üniversitesi Adresli: Evet

Özet

© 2019, Springer-Verlag GmbH Germany, part of Springer Nature.In the linear regression model, the errors are usually assumed to be uncorrelated. However, in real-life data, this assumption is not often plausible. In this study, first, we will assume that the errors of the regression model have autoregressive structure. This type of regression models has been considered before. However, in those papers under this assumption usually, the symmetric distributions are used as error distribution. The main contribution of this work is to use skew distributions instead of symmetric distributions as error distribution in regression models with autoregressive errors. We provide expectation maximization algorithm to compute the maximum likelihood estimates for the parameters. The performances of the proposed estimators are demonstrated with a simulation study and a real data example. We also provide the confidence intervals using the observed Fisher information matrix for the corresponding estimators.