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.

Bayesian quantile regression-based partially linear mixed-effects joint models for longitudinal data with multiple features

2017 ◽  
Vol 28 (2) ◽  
pp. 569-588 ◽  
Author(s):  
Hanze Zhang ◽  
Yangxin Huang ◽  
Wei Wang ◽  
Henian Chen ◽  
Barbara Langland-Orban
Keyword(s):  
Viral Load ◽  
Longitudinal Data ◽  
Quantile Regression ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Asymmetric Distribution ◽  
Joint Models ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects ◽  
Partially Linear

In longitudinal AIDS studies, it is of interest to investigate the relationship between HIV viral load and CD4 cell counts, as well as the complicated time effect. Most of common models to analyze such complex longitudinal data are based on mean-regression, which fails to provide efficient estimates due to outliers and/or heavy tails. Quantile regression-based partially linear mixed-effects models, a special case of semiparametric models enjoying benefits of both parametric and nonparametric models, have the flexibility to monitor the viral dynamics nonparametrically and detect the varying CD4 effects parametrically at different quantiles of viral load. Meanwhile, it is critical to consider various data features of repeated measurements, including left-censoring due to a limit of detection, covariate measurement error, and asymmetric distribution. In this research, we first establish a Bayesian joint models that accounts for all these data features simultaneously in the framework of quantile regression-based partially linear mixed-effects models. The proposed models are applied to analyze the Multicenter AIDS Cohort Study (MACS) data. Simulation studies are also conducted to assess the performance of the proposed methods under different scenarios.

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