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|a 10.1080/02664763.2023.2248418
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|a eng
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|a Kuran, Özge
|e verfasserin
|4 aut
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|a Kernel mixed and Kernel stochastic restricted ridge predictions in the partially linear mixed measurement error models
|b an application to COVID-19
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|c 2024
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 19.08.2024
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|a published: Electronic-eCollection
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|a Citation Status PubMed-not-MEDLINE
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|a © 2023 Informa UK Limited, trading as Taylor & Francis Group.
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|a In this article, we define mixed predictor and stochastic restricted ridge predictor of partially linear mixed measurement error models by taking advantage of Kernel approximation. Under matrix mean square error criterion, we make the comparison of the superiorities the linear combinations of the new defined predictors. Then we investigate the asymptotic normality characteristics and the situation of the unknown covariance matrix of measurement errors. Finally, the study is ended with a Monte Carlo simulation study and COVID-19 data application
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|a Journal Article
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|a Kernel mixed predictor
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| 650 |
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|a Kernel stochastic restricted ridge predictor
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| 650 |
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|a Multicollinearity
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| 650 |
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|a asymptotic normality
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| 650 |
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|a partially linear mixed measurement error models
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1 |
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|a Yalaz, Seçil
|e verfasserin
|4 aut
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|i Enthalten in
|t Journal of applied statistics
|d 1991
|g 51(2024), 10 vom: 13., Seite 1894-1918
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|x 0266-4763
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|g volume:51
|g year:2024
|g number:10
|g day:13
|g pages:1894-1918
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|u http://dx.doi.org/10.1080/02664763.2023.2248418
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