Electronic Journal of Statistics

Penalized regression, mixed effects models and appropriate modelling

Nancy Heckman, Richard Lockhart, and Jason D. Nielsen

Full-text: Open access

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.

Article information

Source
Electron. J. Statist., Volume 7 (2013), 1517-1552.

Dates
Received: May 2012
First available in Project Euclid: 29 May 2013

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1369836229

Digital Object Identifier
doi:10.1214/13-EJS809

Mathematical Reviews number (MathSciNet)
MR3066377

Zentralblatt MATH identifier
1327.62256

Subjects
Primary: 62G08: Nonparametric regression
Secondary: 62J99: None of the above, but in this section

Keywords
Linear mixed effects models penalized smoothing P-splines sandwich estimator

Citation

Heckman, Nancy; Lockhart, Richard; Nielsen, Jason D. Penalized regression, mixed effects models and appropriate modelling. Electron. J. Statist. 7 (2013), 1517--1552. doi:10.1214/13-EJS809. https://projecteuclid.org/euclid.ejs/1369836229


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Supplemental materials

  • Supplement to “Penalized regression, mixed effects models and appropriate modelling”. The Supplementary Material includes code to obtain estimates of $\mu$ and standard errors, as described in Sections 3 and 4. Also included are: code to produce plots from the paper; code to generate simulated data and to run the simulation for our methods; code to simulate according to the methods of Djeundje and Currie [4]; details of the results of the simulation study; a description of the analysis of the Canadian weather data, assuming that $\mu$ is random, and accompanying code; results of the analysis of the fruit fly data, for both $\mu$ non-random and $\mu$ random.