scholarly journals Joint mixed-effects models for causal inference with longitudinal data

2017 ◽  
Vol 37 (5) ◽  
pp. 829-846 ◽  
Author(s):  
Michelle Shardell ◽  
Luigi Ferrucci
Keyword(s):  
Longitudinal Data ◽  
Causal Inference ◽  
Mixed Effects ◽  
Mixed Effects Models

Kernels for Longitudinal Data with Variable Sequence Length and Sampling Intervals

2011 ◽  
Vol 23 (9) ◽  
pp. 2390-2420 ◽  
Author(s):  
Zhengdong Lu ◽  
Todd K. Leen ◽  
Jeffrey Kaye
Keyword(s):  
Longitudinal Data ◽  
Cognitive Decline ◽  
Likelihood Ratio ◽  
Neuropsychological Tests ◽  
Motor Behavior ◽  
Generative Models ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Sequence Length ◽  
Better Than

We develop several kernel methods for classification of longitudinal data and apply them to detect cognitive decline in the elderly. We first develop mixed-effects models, a type of hierarchical empirical Bayes generative models, for the time series. After demonstrating their utility in likelihood ratio classifiers (and the improvement over standard regression models for such classifiers), we develop novel Fisher kernels based on mixture of mixed-effects models and use them in support vector machine classifiers. The hierarchical generative model allows us to handle variations in sequence length and sampling interval gracefully. We also give nonparametric kernels not based on generative models, but rather on the reproducing kernel Hilbert space. We apply the methods to detecting cognitive decline from longitudinal clinical data on motor and neuropsychological tests. The likelihood ratio classifiers based on the neuropsychological tests perform better than than classifiers based on the motor behavior. Discriminant classifiers performed better than likelihood ratio classifiers for the motor behavior tests.

scholarly journals Doubly regularized estimation and selection in linear mixed-effects models for high-dimensional longitudinal data

2018 ◽  
Vol 11 (4) ◽  
pp. 721-737 ◽  
Author(s):  
Yun Li ◽  
Sijian Wang ◽  
Peter X.-K. Song ◽  
Naisyin Wang ◽  
Ling Zhou ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
High Dimensional ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects ◽  
Regularized Estimation

scholarly journals The Demands of a Professional Ballet Schedule: A Five-Season Analysis

2021 ◽  
Author(s):  
Joseph William Shaw ◽  
Adam Mattiussi ◽  
Derrick Dewayne Brown ◽  
Sean Williams ◽  
Matthew Springham ◽  
...  
Keyword(s):  
Random Forest ◽  
Longitudinal Data ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects ◽  
Factors Associated ◽  
Seasonal Performance ◽  
And Performance

Periodizing rehearsal and performance schedules in professional ballet is difficult given a lack of published longitudinal data. We aimed to describe the structure of a professional ballet season, and identify factors associated with inter-dancer and inter-production variation in dance hours. Scheduling data were collected from 123 dancers over five seasons at The Royal Ballet. Linear mixed effects models were used to evaluate differences in weekly dance hours and performance counts across sexes, company ranks, and months. Random forest regressions were used to investigate factors associated with the variation in rehearsal hours across different productions. Performance congestion was observed in December, whereas total dance hours peaked between January and April. Differences in weekly dance hours were observed between company ranks (p < .001, range in means: 19.1–27.5 h·week-1). Seasonal performance counts varied across company ranks (p < .001), ranging from 28, 95% CI [22, 35] in principals, to 113, 95% CI [108, 118] in artists. Rehearsal durations were greatest in preparation for newly choreographed and longer ballets. Dancers creating roles in new ballets completed considerably more rehearsal hours than for existing ballet. These results provide a basis for the implementation of rehearsal and repertoireperiodization in professional ballet.

Sign in / Sign up

Export Citation Format

Share Document