IMPROVING ESTIMATIONS IN QUANTILE REGRESSION MODEL WITH AUTOREGRESSIVE ERRORS

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dc.authoridAsar, Yasin/0000-0003-1109-8456
dc.authoridYuzbasi, Bahadir/0000-0002-6196-3201
dc.authoridDemiralp, Ahmet/0000-0002-0981-7215
dc.authorwosidAsar, Yasin/V-5701-2017
dc.authorwosidYuzbasi, Bahadir/F-6907-2013
dc.authorwosidDemiralp, Ahmet/V-5436-2017
dc.contributor.authorYuzbasi, Bahadir
dc.contributor.authorAsar, Yasin
dc.contributor.authorSik, M. Samil
dc.contributor.authorDemiralp, Ahmet
dc.date.accessioned2024-08-04T20:44:29Z
dc.date.available2024-08-04T20:44:29Z
dc.date.issued2018
dc.departmentİnönü Üniversitesien_US
dc.description.abstractAn important issue is that the respiratory mortality may be a result of air pollution which can be measured by the following variables: temperature, relative humidity, carbon monoxide, sulfur dioxide, nitrogen dioxide, hydrocarbons, ozone, and particulates. The usual way is to fit a model using the ordinary least squares regression, which has some assumptions, also known as Gauss-Markov assumptions, on the error term showing white noise process of the regression model. However, in many applications, especially for this example, these assumptions are not satisfied. Therefore, in this study, a quantile regression approach is used to model the respiratory mortality using the mentioned explanatory variables. Moreover, improved estimation techniques such as preliminary testing and shrinkage strategies are also obtained when the errors are autoregressive. A Monte Carlo simulation experiment, including the quantile penalty estimators such as lasso, ridge, and elastic net, is designed to evaluate the performances of the proposed techniques. Finally, the theoretical risks of the listed estimators are given.en_US
dc.description.sponsorshipInonu University [SBA-2017-946]en_US
dc.description.sponsorshipWe would like to thank two anonymous reviewers for constructive comments which significantly improved the presentation of paper and let to put many details. This work is supported by Research Fund of the Inonu University with project number: SBA-2017-946.en_US
dc.identifier.doi10.2298/TSCI170612275Y
dc.identifier.endpageS107en_US
dc.identifier.issn0354-9836
dc.identifier.issn2334-7163
dc.identifier.scopus2-s2.0-85046874726en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpageS97en_US
dc.identifier.urihttps://doi.org/10.2298/TSCI170612275Y
dc.identifier.urihttps://hdl.handle.net/11616/98285
dc.identifier.volume22en_US
dc.identifier.wosWOS:000431094700012en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherVinca Inst Nuclear Scien_US
dc.relation.ispartofThermal Scienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectpreliminary estimationen_US
dc.subjectStein-type estimationen_US
dc.subjectautocorrelationen_US
dc.subjectquantile regressionen_US
dc.titleIMPROVING ESTIMATIONS IN QUANTILE REGRESSION MODEL WITH AUTOREGRESSIVE ERRORSen_US
dc.typeArticleen_US

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