Abstract
Semiparametric mixed model analysis benefits from variability estimates such as standard errors of effect estimates and variability bars to accompany curve estimates. We show how the underlying variance calculations can be done extremely efficiently compared with the direct naïve approach. These streamlined calculations are linear in the number of subjects, representing a two orders of magnitude improvement.
Highlights
A current vibrant area of research is the use of nonparametric regression, or smoothing, techniques in the analysis of longitudinal data
It consists of longitudinal measurements on the spinal bone mineral density (SBMD) of a cohort of young female subjects
The use of semiparametric regression in the analysis of longitudinal data has become commonplace in the last decade
Summary
A current vibrant area of research is the use of nonparametric regression, or smoothing, techniques in the analysis of longitudinal data. It consists of longitudinal measurements on the spinal bone mineral density (SBMD) of a cohort of young female subjects (source: Reference [9]). The key is recognition that the contribution to M from the random intercept component is an m × m diagonal matrix Such streamlining essentially removes computational obstacles involving variances for models such as (1) for most practical values of m and, greatly benefits semiparametric mixed model analysis.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
