Rank dynamics for functional data

Chen, Yaqing; Dawson, Matthew; Müller, Hans‐Georg

doi:10.1016/j.csda.2020.106963

Cited by 5 publications

(4 citation statements)

References 38 publications

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“…It is useful to complement these comparisons by ranking each country by cumulative case counts per million, where higher percentile ranks correspond to increased infection rates and a generally worse situation. Ranking can be done at each fixed time and then analyzed with rank dynamics 32 . The percentile ranks and their time-evolution are illustrated in Fig.…”

Section: Resultsmentioning

confidence: 99%

Time dynamics of COVID-19

Carroll

Bhattacharjee

Chen

et al. 2020

Sci Rep

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We apply tools from functional data analysis to model cumulative trajectories of COVID-19 cases across countries, establishing a framework for quantifying and comparing cases and deaths across countries longitudinally. It emerges that a country’s trajectory during an initial first month “priming period” largely determines how the situation unfolds subsequently. We also propose a method for forecasting case counts, which takes advantage of the common, latent information in the entire sample of curves, instead of just the history of a single country. Our framework facilitates to quantify the effects of demographic covariates and social mobility on doubling rates and case fatality rates through a time-varying regression model. Decreased workplace mobility is associated with lower doubling rates with a roughly 2 week delay, and case fatality rates exhibit a positive feedback pattern.

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Section: Resultsmentioning

confidence: 99%

Time dynamics of COVID-19

Carroll

Bhattacharjee

Chen

et al. 2020

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is useful to complement these comparisons by ranking each country by cumulative cases counts per million, where higher percentile ranks correspond to increased infection rates and a generally worse situation. Such rankings can be done at each fixed time and then analyzed with rank dy- namics [28]. The percentile ranks and their time-evolutions are illustrated in Figure 4 with a few notable curves highlighted.…”

Section: Comparisons Via Rank Dynamicsmentioning

confidence: 99%

Time Dynamics of COVID-19

Carroll

Bhattacharjee

Chang

et al. 2020

Preprint

Self Cite

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We apply tools from functional data analysis to model cumulative trajectories of COVID-19 cases across countries, establishing a framework for quantifying and comparing cases and deaths across countries longitudinally. It emerges that a country's trajectory during an initial first month "priming period" largely determines how the situation unfolds subsequently. We also propose a method for forecasting case counts, which takes advantage of the common, latent information in the entire sample of curves, instead of just the history of a single country. Our framework facilitates to quantify the effects of demographic covariates and social mobility on doubling rates and case fatality rates through a time-varying regression model. Decreased workplace mobility is associated with lower doubling rates with a roughly two week delay, and case fatality rates exhibit a positive feedback pattern.

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“…Gabel and Redner (2012) shows that NBA basketball score differences are well described by a continuous-time anti-persistent random walk which suggests that a latent Gaussian process might be viable. Chen et al (2020) consider a functional data model for dynamic behavior of cross-sectional ranks over time. While this approach can disentangle the individual and population effect on the ranks of the individual teams over time its setup is not really geared toward analyzing single matches.…”

Section: Introductionmentioning

confidence: 99%

Having a ball: evaluating scoring streaks and game excitement using in-match trend estimation

Ekstrøm

Jensen

2022

AStA Adv Stat Anal

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Many popular sports involve matches between two teams or players where each team have the possibility of scoring points throughout the match. While the overall match winner and result is interesting, it conveys little information about the underlying scoring trends throughout the match. Modeling approaches that accommodate a finer granularity of the score difference throughout the match is needed to evaluate in-game strategies, discuss scoring streaks, teams strengths, and other aspects of the game. We propose a latent Gaussian process to model the score difference between two teams and introduce the Trend Direction Index as an easily interpretable probabilistic measure of the current trend in the match as well as a measure of post-game trend evaluation. In addition we propose the Excitement Trend Index-the expected number of monotonicity changes in the running score difference-as a measure of overall game excitement. Our proposed methodology is applied to all 1143 matches from the 2019-2020 National Basketball Association season. We show how the trends can be interpreted in individual games and how the excitement score can be used to cluster teams according to how exciting they are to watch.

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Rank dynamics for functional data

Cited by 5 publications

References 38 publications

Time dynamics of COVID-19

Time dynamics of COVID-19

Time Dynamics of COVID-19

Having a ball: evaluating scoring streaks and game excitement using in-match trend estimation

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