Improving prediction by means of a two parameter approach in linear mixed models

Kuran, Özge; Özbay, Nimet

doi:10.1080/00949655.2021.1946540

Journal of Statistical Computation and Simulation

2021

DOI: 10.1080/00949655.2021.1946540

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Improving prediction by means of a two parameter approach in linear mixed models

Özge Kuran

Nimet Özbay

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Cited by 3 publications

References 23 publications

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On the performance of two-parameter ridge estimators for handling multicollinearity problem in linear regression: Simulation and application

Khan,

Ali,

Suhail

et al. 2023

AIP Advances

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The inability of ordinary least square estimators against multicollinearity has paved the way for the development of various ridge-type estimators, which are recently classified as one-parameter and two-parameter ridge estimators. In this paper, we offer some efficient two-parameter ridge estimators and evaluate their performance through a simulation study by using the minimum mean square error criterion. Under most of the simulation conditions, our proposed estimators outperformed the existing estimators. Finally, two real-life datasets are used to demonstrate the applications of our proposed estimators.

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On the performance of two-parameter ridge estimators for handling multicollinearity problem in linear regression: Simulation and application

Khan,

Ali,

Suhail

et al. 2023

AIP Advances

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A new kernel two-parameter prediction under multicollinearity in partially linear mixed measurement error model

Yalaz,

Kuran

2024

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On some two parameter estimators for the linear regression models with correlated predictors: simulation and application

Khan,

Ali,

Suhail

et al. 2024

Communications in Statistics - Simulation and Computation

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Improving prediction by means of a two parameter approach in linear mixed models

Cited by 3 publications

References 23 publications

On the performance of two-parameter ridge estimators for handling multicollinearity problem in linear regression: Simulation and application

On the performance of two-parameter ridge estimators for handling multicollinearity problem in linear regression: Simulation and application

A new kernel two-parameter prediction under multicollinearity in partially linear mixed measurement error model

On some two parameter estimators for the linear regression models with correlated predictors: simulation and application

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