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Adaptive Estimation of Autoregressive Models Under Long-Tailed Symmetric Distribution

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dc.contributor.author Yengür, Begüm
dc.contributor.author Bayrak, Özlem Türker
dc.contributor.author Dener Akkaya, Ayşen
dc.date.accessioned 2020-05-08T11:41:56Z
dc.date.available 2020-05-08T11:41:56Z
dc.date.issued 2019-07-08
dc.identifier.citation Yentür, B.; Bayrak, Ö.T.; Akkaya, A.D. (2019). "Adaptive Estimation of Autoregressive Models Under Long-Tailed Symmetric Distribution", Acm International Conference Proceeding Series, pp. 68-72. tr_TR
dc.identifier.isbn 978-145037168-1
dc.identifier.uri http://hdl.handle.net/20.500.12416/3654
dc.description.abstract In this paper, we consider the autoregressive models where the error term is non-normal; specifically belongs to a long-tailed symmetric distribution family since it is more relevant in practice than the normal distribution. It is known that least squares (LS) estimators are neither efficient nor robust under non-normality and maximum likelihood (ML) estimators cannot be obtained explicitly and require a numerical solution which might be problematic. In recent years, modified maximum likelihood (MML) estimation is developed to overcome these difficulties. However, this method requires that the shape parameter is known which is not realistic in machine data processing. Therefore, we use adaptive modified maximum likelihood (AMML) technique which combines MML with Huber’s estimation procedure so that the shape parameter is also estimated. After derivation of the AMML estimators, their efficiency and robustness properties are discussed through a simulation study and compared with MML and LS estimators. tr_TR
dc.language.iso eng tr_TR
dc.publisher Association for Computing Machinery tr_TR
dc.relation.isversionof 10.1145/3343485.3343490 tr_TR
dc.rights info:eu-repo/semantics/openAccess tr_TR
dc.subject Modified Maximum Likelihood tr_TR
dc.subject Autocorrelation tr_TR
dc.subject Robust tr_TR
dc.subject Regression tr_TR
dc.title Adaptive Estimation of Autoregressive Models Under Long-Tailed Symmetric Distribution tr_TR
dc.type conferenceObject tr_TR
dc.relation.journal Acm International Conference Proceeding Series tr_TR
dc.contributor.authorID 56416 tr_TR
dc.identifier.startpage 68 tr_TR
dc.identifier.endpage 72 tr_TR
dc.contributor.department Çankaya Üniversitesi, İktisadi İdari Bilimler Fakültesi, İstatistik Bilim Dalı tr_TR


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