Improved EBLUPs in Mixed-Effects Regression Models
Sam W,
Peijin X,
Ching Ry and
Kelly Hz
Additional contact information
Sam W: Pfizer Inc., New York, USA
Peijin X: Hershey, PA, New York, USA
Ching Ry: 1Pfizer Inc., New York, USA
Kelly Hz: Pfizer Inc., New York, USA
Biostatistics and Biometrics Open Access Journal, 2018, vol. 4, issue 4, 78-86
Abstract:
Mixed models are now heavily employed in business analysis, studies of public health, and in clinical research. However, widely used MLE based methods, the ML and the REML, for making inferences about the Best Linear Unbiased Predictor (BLUP) suffers from a number of drawbacks such as the non-convergence and lack of accuracy with small number of factor levels in a group structure. The BLUP in mixed models is a function of the variance components, which are typically estimated by MLE based method.
Keywords: Biometrics Open Access Journal; Biostatistics and Biometrics; Biostatistics and Biometrics Open Access Journal; Open Access Journals; biometrics journal; biometrics articles; biometrics journal reference; biometrics journal impact factor; biometrics and biostatistics journal impact factor; journal of biometrics; open access juniper publishers; juniper publishers reivew (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)
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Persistent link: https://EconPapers.repec.org/RePEc:adp:jbboaj:v:4:y:2018:i:4:p:78-86
DOI: 10.19080/BBOAJ.2018.04.555641
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