Kernel Liu prediction approach in partially linear mixed measurement error models

Kuran, Özge; Yalaz, Seçil

doi:10.1080/02331888.2022.2152816

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Covid-19 Data Analysis1

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2024

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Cited by 2 publications

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“…2. After the 𝑘 ̂ value is found from the point 1, the Liu biasing parameter 𝑑 is selected as 𝑑 ̂ℎ which is given by Theorem 4.2 [11] where ℎ is determined as multiplying the upper bound defined in Theorem 4.2 by 0.99 if…”

Section: Covid-19 Data Analysismentioning

confidence: 99%

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

KURAN,

YALAZ

2024

DEUFMD

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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.

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Section: Covid-19 Data Analysismentioning

confidence: 99%

“…Another popular attempt is Liu's approach [8] in LMs. With the help of [9] in LMs and [10] in LMMs, the Kernel Liu predictors which are the Kernel Liu estimator and predictor in PLMMeMs for a given Liu biasing parameter 0 < 𝑑 < 1 are given by [11], respectively as…”

Section: Introductionmentioning

confidence: 99%