Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Özge Kuran"'
Autor:
Özge Kuran, Seçil Yalaz
Publikováno v:
Statistics. 56:1385-1408
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b56e9daaddf8e8ec196123ed9d3800b0
https://doi.org/10.1080/02331888.2022.2152816
https://doi.org/10.1080/02331888.2022.2152816
Autor:
M. Revan Özkale, Özge Kuran
Publikováno v:
Concurrency and Computation: Practice and Experience. 34
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e2742949c416ece7d6c6f505c89a8306
https://doi.org/10.1002/cpe.7366
https://doi.org/10.1002/cpe.7366
Autor:
Özge Kuran, M. Revan Özkale
Publikováno v:
Communications in Statistics - Simulation and Computation. 50:2561-2580
In this article, we propose the stochastic restricted Liu predictors by augmenting the stochastic restrictions to the linear mixed models. The Liu biasing parameter is selected via generalized cross validation (GCV) criterion. Comparisons between the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6caa04910390c25f0d9b8e407c547543
https://doi.org/10.1080/03610918.2021.1957110
https://doi.org/10.1080/03610918.2021.1957110
Autor:
M. Revan Özkale, Özge Kuran
Publikováno v:
J Appl Stat
In this paper, we introduce stochastic-restricted Liu predictors which will be defined by combining in a special way the two approaches followed in obtaining the mixed predictors and the Liu predictors in the linear mixed models. Superiorities of the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8e6dc2fa8cdd053224bcff949facc24e
https://pubmed.ncbi.nlm.nih.gov/35707446
https://pubmed.ncbi.nlm.nih.gov/35707446
Autor:
Özge Kuran, Seçil Yalaz
Publikováno v:
Journal of Statistical Computation and Simulation. 91:934-951
This paper considers the partially linear mixed-effect model relating a response Y to predictors ( X , Z , T ) with mean function X T β + Z T b + g ( T ) which is a combination of the linear mixed-...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::472303224b454085d8329f233413a09d
https://doi.org/10.1080/00949655.2020.1836642
https://doi.org/10.1080/00949655.2020.1836642
Autor:
Özge Kuran, Seçil Yalaz
Publikováno v:
Cumhuriyet Science Journal, Vol 41, Iss 3, Pp 571-579 (2020)
In this article, we propose Kernel prediction in partially linear mixed models by using Henderson's method approach. We derive the Kernel estimator and the Kernel predictor via the mixed model equations (MMEs) of Henderson's that they give the best l
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e696e7a20b19a530cba29dc4d59d8da2
https://doi.org/10.17776/csj.671812
https://doi.org/10.17776/csj.671812
Autor:
M. Revan Özkale, Özge Kuran
Publikováno v:
Journal of Statistical Computation and Simulation. 89:3413-3452
The purpose of this article is to obtain the jackknifed ridge predictors in the linear mixed models and to examine the superiorities, the linear combinations of the jackknifed ridge predictors over the ridge, principal components regression, r-k clas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b511c6bd4924392da111c551b52b36d7
https://doi.org/10.1080/00949655.2019.1669037
https://doi.org/10.1080/00949655.2019.1669037
Autor:
Özge Kuran, Nimet Özbay
In this article, two parameter estimator and two parameter predictor are defined via the penalized log-likelihood approach in linear mixed models. The recommended approach is quite useful when there is a strong linear relationship among the variables
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f73afb2b4c102157e986af4cf06a1679
https://www.tandfonline.com/doi/full/10.1080/00949655.2021.1946540
https://www.tandfonline.com/doi/full/10.1080/00949655.2021.1946540
Autor:
Özge Kuran, M. Revan Özkale
Publikováno v:
Journal of Statistical Computation and Simulation. 89:155-187
In this paper, we focus on the progress of variant of conceptual predictive (Cp) statistic and we propose the model selection criterion that depend on Cp statistic under ridge regression for linear mixed model selection. The proposed criterion is con
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::661efefb67cbec054c1d316c20f588b5
https://doi.org/10.1080/00949655.2018.1540622
https://doi.org/10.1080/00949655.2018.1540622
Autor:
Özge Kuran, M. Revan Özkale
Publikováno v:
Linear Algebra and its Applications. 508:22-47
This article is concerned with the predictions in linear mixed models under stochastic linear restrictions. Mixed and stochastic restricted ridge predictors are introduced by using Gilmour's approach. We also investigate assumptions that the variance
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::809ce69bfb9dc75da4b342e451149292
https://doi.org/10.1016/j.laa.2016.06.040
https://doi.org/10.1016/j.laa.2016.06.040