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• W2029605311 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." @default.
• W2029605311 created "2016-06-24" @default.
• W2029605311 creator A5035238514 @default.
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• W2029605311 date "2013-01-01" @default.
• W2029605311 modified "2023-10-01" @default.
• W2029605311 title "Penalized regression, mixed effects models and appropriate modelling" @default.
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• W2029605311 doi "https://doi.org/10.1214/13-ejs809" @default.
• W2029605311 hasPublicationYear "2013" @default.
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