Premium
Self‐modelling regression for longitudinal data with time‐invariant covariates
Author(s) -
Altman Naomi,
Villarreal Julio
Publication year - 2004
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.2307/3315928
Subject(s) - covariate , statistics , regression analysis , mathematics , longitudinal data , mixed model , spline (mechanical) , linear regression , computer science , econometrics , data mining , engineering , structural engineering
The authors propose the use of self‐modelling regression to analyze longitudinal data with time invariant covariates. They model the population time curve with a penalized regression spline and use a linear mixed model for transformation of the time and response scales to fit the individual curves. Fitting is done by an iterative algorithm using off‐the‐shelf linear and nonlinear mixed model software. Their method is demonstrated in a simulation study and in the analysis of tree swallow nestling growth from an experiment that includes an experimentally controlled treatment, an observational covariate and multi‐level sampling.