An evaluation of ridge estimator in linear mixed models: an example from kidney failure data

M. Revan Özkale and Funda Can

Journal of Applied Statistics, 2017, vol. 44, issue 12, 2251-2269

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.

Date: 2017
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DOI: 10.1080/02664763.2016.1252732

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