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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
References
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- Kuran, Ö., Yalaz, S. 2022. Kernel Ridge Prediction Method in Partially Linear Mixed Measurement Error Model, Communications in Statistics - Simulation and Computation, Vol., No., p. 1–22, DOI:10.1080/03610918.2022.2075389.
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Details
Primary Language
English
Subjects
Numerical Analysis
Journal Section
Research Article
Early Pub Date
January 22, 2024
Publication Date
January 23, 2024
Submission Date
January 13, 2023
Acceptance Date
July 31, 2023
Published in Issue
Year 2024 Volume: 26 Number: 76
APA
Kuran, Ö., & Yalaz, S. (2024). Performance Assessment of the Modified Kernel Ridge Predictors in the Partially Linear Mixed Measurement Error Models via Covid-19 Data Analysis. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi, 26(76), 134-140. https://doi.org/10.21205/deufmd.2024267615
AMA
1.Kuran Ö, Yalaz S. Performance Assessment of the Modified Kernel Ridge Predictors in the Partially Linear Mixed Measurement Error Models via Covid-19 Data Analysis. DEUFMD. 2024;26(76):134-140. doi:10.21205/deufmd.2024267615
Chicago
Kuran, Özge, and Seçil Yalaz. 2024. “Performance Assessment of the Modified Kernel Ridge Predictors in the Partially Linear Mixed Measurement Error Models via Covid-19 Data Analysis”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi 26 (76): 134-40. https://doi.org/10.21205/deufmd.2024267615.
EndNote
Kuran Ö, Yalaz S (January 1, 2024) Performance Assessment of the Modified Kernel Ridge Predictors in the Partially Linear Mixed Measurement Error Models via Covid-19 Data Analysis. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi 26 76 134–140.
IEEE
[1]Ö. Kuran and S. Yalaz, “Performance Assessment of the Modified Kernel Ridge Predictors in the Partially Linear Mixed Measurement Error Models via Covid-19 Data Analysis”, DEUFMD, vol. 26, no. 76, pp. 134–140, Jan. 2024, doi: 10.21205/deufmd.2024267615.
ISNAD
Kuran, Özge - Yalaz, Seçil. “Performance Assessment of the Modified Kernel Ridge Predictors in the Partially Linear Mixed Measurement Error Models via Covid-19 Data Analysis”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi 26/76 (January 1, 2024): 134-140. https://doi.org/10.21205/deufmd.2024267615.
JAMA
1.Kuran Ö, Yalaz S. Performance Assessment of the Modified Kernel Ridge Predictors in the Partially Linear Mixed Measurement Error Models via Covid-19 Data Analysis. DEUFMD. 2024;26:134–140.
MLA
Kuran, Özge, and Seçil Yalaz. “Performance Assessment of the Modified Kernel Ridge Predictors in the Partially Linear Mixed Measurement Error Models via Covid-19 Data Analysis”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi, vol. 26, no. 76, Jan. 2024, pp. 134-40, doi:10.21205/deufmd.2024267615.
Vancouver
1.Özge Kuran, Seçil Yalaz. Performance Assessment of the Modified Kernel Ridge Predictors in the Partially Linear Mixed Measurement Error Models via Covid-19 Data Analysis. DEUFMD. 2024 Jan. 1;26(76):134-40. doi:10.21205/deufmd.2024267615