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A New Estimation Technique for AR(1) Model with Long-Tailed Symmetric Innovations

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dc.contributor.author Dener Akkaya, Ayşen
dc.contributor.author Türker Bayrak, Özlem
dc.date.accessioned 2020-12-23T11:05:34Z
dc.date.available 2020-12-23T11:05:34Z
dc.date.issued 2018
dc.identifier.citation Dener Akkaya, Ayşen; Türker Bayrak, Özlem. "Time Series Analysis and Forecasting: A New Estimation Technique for AR(1) Model with Long-Tailed Symmetric Innovations, pp. 39-63, 2018. tr_TR
dc.identifier.isbn 9783319969442
dc.identifier.isbn 9783319969435
dc.identifier.uri http://hdl.handle.net/20.500.12416/4369
dc.description.abstract In recent years, it is seen in many time series applications that innovations are non-normal. In this situation, it is known that the least squares (LS) estimators are neither efficient nor robust and maximum likelihood (ML) estimators can only be obtained numerically which might be problematic. The estimation problem is considered newly through different distributions by the use of modified maximum likelihood (MML) estimation technique which assumes the shape parameter to be known. This becomes a drawback in machine data processing where the underlying distribution cannot be determined but assumed to be a member of a broad class of distributions. Therefore, in this study, the shape parameter is assumed to be unknown and the MML technique is combined with Huber’s estimation procedure to estimate the model parameters of autoregressive (AR) models of order 1, named as adaptive modified maximum likelihood (AMML) estimation. After the derivation of the AMML estimators, their efficiency and robustness properties are discussed through simulation study and compared with both MML and LS estimators. Besides, two test statistics for significance of the model are suggested. Both criterion and efficiency robustness properties of the test statistics are discussed, and comparisons with the corresponding MML and LS test statistics are given. Finally, the estimation procedure is generalized to AR(q) models. tr_TR
dc.language.iso eng tr_TR
dc.publisher Springer tr_TR
dc.relation.isversionof 10.1007/978-3-319-96944-2_4 tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject Adaptive Modified Maximum Likelihood tr_TR
dc.subject Autoregressive Models tr_TR
dc.subject Least Squares Estimators tr_TR
dc.subject Hypothesis Testing tr_TR
dc.subject Modified Maximum Likelihood tr_TR
dc.subject Estimation tr_TR
dc.subject Efficiency tr_TR
dc.subject Robustness tr_TR
dc.title A New Estimation Technique for AR(1) Model with Long-Tailed Symmetric Innovations tr_TR
dc.type bookPart tr_TR
dc.relation.journal Time Series Analysis and Forecasting tr_TR
dc.contributor.authorID 56416 tr_TR
dc.identifier.startpage 39 tr_TR
dc.identifier.endpage 63 tr_TR
dc.contributor.department Çankaya Üniversitesi, İktisadi ve İdari Bilimler Fakültesi, İstatistik Bilim Dalı tr_TR


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