Ölçüm Hatalı Kısmi Lineer Karma Modellerde Modified Kernel Ridge Öntahmin Edicilerin Covid-19 Veri Analizi Yoluyla Performans Değerlendirmesi

Year 2024, , 134 - 140, 23.01.2024

Özge Kuran , Seçil Yalaz

https://doi.org/10.21205/deufmd.2024267615

Abstract

Bu çalışmada, ölçüm hatalı kısmi lineer karma modellerde çoklu iç ilişki durumu altında yeni öntahmin ediciler tanımlanmaktadır. Bu amaca ulaşmak için, bazı ön bilgiler ele alınmıştır ve bu bilgi hesaba katılarak, ölçüm hatalı kısmi lineer karma modellerde modified Kernel ridge öntahmin edicileri önerilmiştir. Ek olarak, ölçüm hatalı kısmi lineer karma model literatüründe daha önce tanımlanan öntahmin ediciler ile yeni tanımlanan modified Kernel ridge öntahmin ediciler arasında bazı hata kareler ortalama karşılaştırmaları da yapılmıştır. Daha sonra, teorik bulgularımızı kanıtlamak için gerçek bir veri analizi ve simülasyon çalışması ile makale sonlandırılmıştır.

Keywords

Lineer Karma Model, Ölçüm Hatası, Çoklu İç İlişki, Kısmi Lineer Model, Ridge Öntahmin Ediciler

Performance Assessment of the Modified Kernel Ridge Predictors in the Partially Linear Mixed Measurement Error Models via Covid-19 Data Analysis

Abstract

In this article we describe new predictors under multicollinearity situation in the partially linear mixed measurement error models. In order to achieve this aim, we refer to some preliminary information and use it in order to suggest the modified Kernel ridge predictors in the partially linear mixed measurement error models. In addition, we also attain some mean square error comparisons between our new described modified Kernel ridge predictors and predictors previously described in literature for the partially linear mixed measurement error model. In conclusion, the article showcases real data analysis and a simulation study to illusrate our theoretical findings.

Keywords

Linear Mixed Model, Measurement Error, Multicollinearity, Partially Linear Model, Ridge Predictor

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