scholarly journals Small area forecasts of cause-specific mortality: application of a Bayesian hierarchical model to US vital registration data

2016 ◽  
Vol 66 (1) ◽  
pp. 121-139 ◽  
Author(s):  
Kyle J. Foreman ◽  
Guangquan Li ◽  
Nicky Best ◽  
Majid Ezzati
Keyword(s):  
Hierarchical Model ◽  
Small Area ◽  
Bayesian Hierarchical Model ◽  
Vital Registration ◽  
Bayesian Hierarchical ◽  
Registration Data ◽  
Vital Registration Data ◽  
Specific Mortality

Living Same-Name Siblings and British Historical Demography

2011 ◽  
pp. 15-36 ◽  
Author(s):  
Chris Galley ◽  
Eilidh Garrett ◽  
Ros Davies ◽  
Alice Reid
Keyword(s):  
Nineteenth Century ◽  
Eighteenth Century ◽  
Historical Demography ◽  
Vital Registration ◽  
Regional Variations ◽  
Quantitative Evidence ◽  
Registration Data ◽  
Vital Registration Data ◽  
The Rich ◽  
Isle Of Skye

This article examines the extent to which living siblings were given identical first names. Whilst the practice of sibling name-sharing appeared to have died out in England during the eighteenth century, in northern Scotland it persisted at least until the end of the nineteenth century. Previously it has not been possible to provide quantitative evidence of this phenomenon, but an analysis of the rich census and vital registration data for the Isle of Skye reveals that this practice was widespread, with over a third of eligible families recording same-name siblings. Our results suggest that further research should focus on regional variations in sibling name-sharing and the extent to which this northern pattern occurred in other parts of Britain.

scholarly journals Measuring spatial allocative efficiency in basketball

2020 ◽  
Vol 16 (4) ◽  
pp. 271-289
Author(s):  
Nathan Sandholtz ◽  
Jacob Mortensen ◽  
Luke Bornn
Keyword(s):  
Spatial Distribution ◽  
Hierarchical Model ◽  
Opportunity Cost ◽  
Allocative Efficiency ◽  
Bayesian Hierarchical Model ◽  
National Basketball Association ◽  
Spatial Context ◽  
Bayesian Hierarchical ◽  
The Impact

AbstractEvery shot in basketball has an opportunity cost; one player’s shot eliminates all potential opportunities from their teammates for that play. For this reason, player-shot efficiency should ultimately be considered relative to the lineup. This aspect of efficiency—the optimal way to allocate shots within a lineup—is the focus of our paper. Allocative efficiency should be considered in a spatial context since the distribution of shot attempts within a lineup is highly dependent on court location. We propose a new metric for spatial allocative efficiency by comparing a player’s field goal percentage (FG%) to their field goal attempt (FGA) rate in context of both their four teammates on the court and the spatial distribution of their shots. Leveraging publicly available data provided by the National Basketball Association (NBA), we estimate player FG% at every location in the offensive half court using a Bayesian hierarchical model. Then, by ordering a lineup’s estimated FG%s and pairing these rankings with the lineup’s empirical FGA rate rankings, we detect areas where the lineup exhibits inefficient shot allocation. Lastly, we analyze the impact that sub-optimal shot allocation has on a team’s overall offensive potential, demonstrating that inefficient shot allocation correlates with reduced scoring.

A point-based Bayesian hierarchical model to predict the outcome of tennis matches

2019 ◽  
Vol 15 (4) ◽  
pp. 313-325 ◽  
Author(s):  
Martin Ingram
Keyword(s):  
Random Walk ◽  
Hierarchical Model ◽  
Bayesian Hierarchical Model ◽  
Bayesian Hierarchical ◽  
Gaussian Random Walk ◽  
Over Time ◽  
Modelling Approach ◽  
Independent And Identically Distributed

Abstract A well-established assumption in tennis is that point outcomes on each player’s serve in a match are independent and identically distributed (iid). With this assumption, it is enough to specify the serve probabilities for both players to derive a wide variety of event distributions, such as the expected winner and number of sets, and number of games. However, models using this assumption, which we will refer to as “point-based”, have typically performed worse than other models in the literature at predicting the match winner. This paper presents a point-based Bayesian hierarchical model for predicting the outcome of tennis matches. The model predicts the probability of winning a point on serve given surface, tournament and match date. Each player is given a serve and return skill which is assumed to follow a Gaussian random walk over time. In addition, each player’s skill varies by surface, and tournaments are given tournament-specific intercepts. When evaluated on the ATP’s 2014 season, the model outperforms other point-based models, predicting match outcomes with greater accuracy (68.8% vs. 66.3%) and lower log loss (0.592 vs. 0.641). The results are competitive with approaches modelling the match outcome directly, demonstrating the forecasting potential of the point-based modelling approach.

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