Abstract
This paper considers the “ridge autoregression R-estimation” of the AR (p)-model when the parameters of the AR(p)-model is suspected to belong to a linear subspace. Accordingly, we introduce ridge autoregression (RARR) modifications to the usual five R-estimators of the parameters of the AR(p)-model. This class of (RARR)-R-estimators, not only alleviates the problem of multicollinearity in the estimated covariance matrix but also retains their asymptotic dominance properties under a quadratic loss function.
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Acknowledgement
The author thanks the referees for their careful reading of the paper. Also, thanks to Prof. H.L.Koul, a strong researcher for his collaborative research with me on autoregressive models which yielded several pioneering results on this topic.
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Saleh, A. (2014). Ridge Autoregression R-Estimation: Subspace Restriction. In: Lahiri, S., Schick, A., SenGupta, A., Sriram, T. (eds) Contemporary Developments in Statistical Theory. Springer Proceedings in Mathematics & Statistics, vol 68. Springer, Cham. https://doi.org/10.1007/978-3-319-02651-0_8
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DOI: https://doi.org/10.1007/978-3-319-02651-0_8
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