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.
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The authors thank the anonymous reviewers for their careful reading and suggestions of this paper. Their comments and suggestions remarkably improved our paper.
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Appendices
Appendix A
Let \( \underline{\theta } = \left( { \beta_{1} , \beta_{2} , \ldots ,\beta_{M} ,\;\phi_{1} ,\phi_{2} , \ldots ,\phi_{p} ,\sigma^{2} } \right). \) The elements of \( U\left( {a_{t} ;\underline{\theta } } \right) \) are
where \( t = p + 1, \ldots ,N. \)
The second partial derivatives are
where \( t = p + 1, \ldots ,N, j,k = 1,2, \ldots ,M \) and \( r,s = 1,2, \ldots ,p. \)
Appendix B
The elements of \( U^{*} \left( {a_{t} ;\underline{\theta } ,q} \right) \) are
where
The second partial derivatives of conditional Lq-likelihood function can be obtained by substituting the derivatives given in “Appendix A” in Eq. (25).
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Güney, Y., Tuaç, Y., Özdemir, Ş. et al. Conditional maximum Lq-likelihood estimation for regression model with autoregressive error terms. Metrika 84, 47–74 (2021). https://doi.org/10.1007/s00184-020-00774-2
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DOI: https://doi.org/10.1007/s00184-020-00774-2