Robust parameter estimation of regression model with AR(p) error terms

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Tuac Y., GÜNEY Y., ŞENOĞLU B., ARSLAN O.

Communications in Statistics: Simulation and Computation, vol.47, no.8, pp.2343-2359, 2018 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Review
  • Volume: 47 Issue: 8
  • Publication Date: 2018
  • Doi Number: 10.1080/03610918.2017.1343839
  • Journal Name: Communications in Statistics: Simulation and Computation
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.2343-2359
  • Keywords: Autoregressive stationary process, Conditional maximum likelihood, Linear regression, Non-normal distributions, Robust estimation, T-DISTRIBUTION, LIKELIHOOD, AUTOREGRESSIONS
  • Ankara University Affiliated: Yes

Abstract

© 2018, © 2018 Taylor & Francis Group, LLC.In this article, we consider a linear regression model with AR(p) error terms with the assumption that the error terms have a t distribution as a heavy-tailed alternative to the normal distribution. We obtain the estimators for the model parameters by using the conditional maximum likelihood (CML) method. We conduct an iteratively reweighting algorithm (IRA) to find the estimates for the parameters of interest. We provide a simulation study and three real data examples to illustrate the performance of the proposed robust estimators based on t distribution.