Çankaya GCRIS Standart veritabanının içerik oluşturulması ve kurulumu Research Ecosystems (https://www.researchecosystems.com) tarafından devam etmektedir. Bu süreçte gördüğünüz verilerde eksikler olabilir.
 

Autoregressive Models With Stochastic Design Variables and Nonnormal İnnovations

dc.contributor.author Türker Bayrak, Özlem
dc.contributor.author Dener Akkaya, Ayşen
dc.contributor.authorID 56416 tr_TR
dc.contributor.authorID 2337 tr_TR
dc.date.accessioned 2020-05-16T15:34:52Z
dc.date.available 2020-05-16T15:34:52Z
dc.date.issued 2011
dc.department Çankaya Üniversitesi, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü en_US
dc.description.abstract In autoregression models the design variable has traditionally been assumed to be non-stochastic and innovations are normal. In most real life situations, however, the design variable is stochastic having a non-normal distribution as the innovations. Modified maximum likelihood method is utilized to estimate unknown parameters in such situations. Closed form estimators are obtained and shown to be efficient and robust. en_US
dc.identifier.citation Bayrak, O.T.; Akkaya, A.D.,"Autoregressive Models With Stochastic Design Variables and Nonnormal İnnovations",International Conference On Applied Mathematics, Simulation, Modelling - Proceedings, (2011). en_US
dc.identifier.endpage 201 en_US
dc.identifier.issn 17924332
dc.identifier.issn 978-161804016-9
dc.identifier.startpage 197 en_US
dc.identifier.uri https://hdl.handle.net/20.500.12416/3876
dc.language.iso en en_US
dc.relation.ispartof International Conference On Applied Mathematics, Simulation, Modelling - Proceedings en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.subject Nonnormality en_US
dc.subject Modified Maximum Likelihood en_US
dc.subject Stochastic Design en_US
dc.subject Robustness en_US
dc.title Autoregressive Models With Stochastic Design Variables and Nonnormal İnnovations tr_TR
dc.title Autoregressive Models With Stochastic Design Variables and Nonnormal İnnovations en_US
dc.type Conference Object en_US
dspace.entity.type Publication

Files

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description: