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

Yeşim Güney (), Y. Tuaç (), Ş. Özdemir () and O. Arslan ()
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Yeşim Güney: Ankara University
Y. Tuaç: Ankara University
Ş. Özdemir: Afyon Kocatepe University
O. Arslan: Ankara University

Metrika: International Journal for Theoretical and Applied Statistics, 2021, vol. 84, issue 1, No 3, 47-74

Abstract: Abstract 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.

Keywords: Autoregressive stationary process; Conditional maximum Lq-likelihood; Linear regression (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s00184-020-00774-2

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