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2013 Penalized regression, mixed effects models and appropriate modelling
Nancy Heckman, Richard Lockhart, Jason D. Nielsen
Electron. J. Statist. 7: 1517-1552 (2013). DOI: 10.1214/13-EJS809

Abstract

Linear mixed effects methods for the analysis of longitudinal data provide a convenient framework for modelling within-individual correlation across time. Using spline functions allows for flexible modelling of the response as a smooth function of time. A computational connection between linear mixed effects modelling and spline smoothing has resulted in a cross-fertilization of these two fields. The connection has popularized the use of spline functions in longitudinal data analysis and the use of mixed effects software in smoothing analyses. However, care must be taken in exploiting this connection, as resulting estimates of the underlying population mean might not track the data well and associated standard errors might not reflect the true variability in the data. We discuss these shortcomings and suggest some easy-to-compute methods to eliminate them.

Citation

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Nancy Heckman. Richard Lockhart. Jason D. Nielsen. "Penalized regression, mixed effects models and appropriate modelling." Electron. J. Statist. 7 1517 - 1552, 2013. https://doi.org/10.1214/13-EJS809

Information

Received: 1 May 2012; Published: 2013
First available in Project Euclid: 29 May 2013

zbMATH: 1327.62256
MathSciNet: MR3066377
Digital Object Identifier: 10.1214/13-EJS809

Subjects:
Primary: 62G08
Secondary: 62J99

Rights: Copyright © 2013 The Institute of Mathematical Statistics and the Bernoulli Society

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