An evaluation of ridge estimator in linear mixed models: an example from kidney failure data |
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Authors: | M. Revan Özkale Funda Can |
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Affiliation: | 1. Faculty of Science and Letters, Department of Statistics, ?ukurova University, Adana, Turkeymrevan@cu.edu.tr;3. Turkish Public Health Association, Assistant Health Specialists, Tr Minister of Health, Ankara, Turkey |
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Abstract: | This paper is concerned with the ridge estimation of fixed and random effects in the context of Henderson's mixed model equations in the linear mixed model. For this purpose, a penalized likelihood method is proposed. A linear combination of ridge estimator for fixed and random effects is compared to a linear combination of best linear unbiased estimator for fixed and random effects under the mean-square error (MSE) matrix criterion. Additionally, for choosing the biasing parameter, a method of MSE under the ridge estimator is given. A real data analysis is provided to illustrate the theoretical results and a simulation study is conducted to characterize the performance of ridge and best linear unbiased estimators approach in the linear mixed model. |
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Keywords: | Ridge regression random effects linear mixed model variance modeling mean-square error |
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