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A Bayesian Joint Model for Spatial Point Processes with Application to Basketball Shot Chart
The success rate of a basketball shot may be higher at locations where a...
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Bayesian Nonparametric Nonhomogeneous Poisson Process with Applications to USGS Earthquake Data
Intensity estimation is a common problem in statistical analysis of spat...
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Analysis of professional basketball field goal attempts via a Bayesian matrix clustering approach
We propose a Bayesian nonparametric matrix clustering approach to analyz...
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Bayesian Group Learning for Shot Selection of Professional Basketball Players
In this paper, we develop a group learning approach to analyze the under...
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Heterogeneity Pursuit for Spatial Point Pattern with Application to Tree Locations: A Bayesian Semiparametric Recourse
Spatial point pattern data are routinely encountered. A flexible regress...
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Burglary in London: Insights from Statistical Heterogeneous Spatial Point Processes
To obtain operational insights regarding the crime of burglary in London...
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Bayesian Characterizations of Properties of Stochastic Processes with Applications
In this article, we primarily propose a novel Bayesian characterization ...
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Bayesian Nonparametric Estimation for Point Processes with Spatial Homogeneity: A Spatial Analysis of NBA Shot Locations
Basketball shot location data provide valuable summary information regarding players to coaches, sports analysts, fans, statisticians, as well as players themselves. Represented by spatial points, such data are naturally analyzed with spatial point process models. We present a novel nonparametric Bayesian method for learning the underlying intensity surface built upon a combination of Dirichlet process and Markov random field. Our method has the advantage of effectively encouraging local spatial homogeneity when estimating a globally heterogeneous intensity surface. Posterior inferences are performed with an efficient Markov chain Monte Carlo (MCMC) algorithm. Simulation studies show that the inferences are accurate and that the method is superior compared to the competing methods. Application to the shot location data of 20 representative NBA players in the 2017-2018 regular season offers interesting insights about the shooting patterns of these players. A comparison against the competing method shows that the proposed method can effectively incorporate spatial contiguity into the estimation of intensity surfaces.
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