activity
20192025
most citedA Bayesian marked spatial point processes model for basketball shot chart

19 citations · 27 across the 5 of their papers we have counts for

collaborators

5 papers

stat.ME2025

Heteroscedastic Growth Curve Modeling with Shape-Restricted Splines

Jieying Jiao, Wenling Song, Yishu Xue +1

Growth curve analysis (GCA) has a wide range of applications in various fields where growth trajectories need to be modeled. Heteroscedasticity is often present in the error term,…

stat.ME2020★ 2 cited

Bayesian Nonparametric Estimation for Point Processes with Spatial Homogeneity: A Spatial Analysis of NBA Shot Locations

Fan Yin, Jieying Jiao, Guanyu Hu +1

Basketball shot location data provide valuable summary information regarding players to coaches, sports analysts, fans, statisticians, as well as players themselves. Represented by…

stat.ME2020★ 5 cited

Heterogeneity Pursuit for Spatial Point Pattern with Application to Tree Locations: A Bayesian Semiparametric Recourse

Jieying Jiao, Guanyu Hu, Jun Yan

Spatial point pattern data are routinely encountered. A flexible regression model for the underlying intensity is essential to characterizing the spatial point pattern and understa…

eess.SY2020★ 1 cited

Enabling Cyberattack-Resilient Load Forecasting through Adversarial Machine Learning

Zefan Tang, Jieying Jiao, Peng Zhang +3

In the face of an increasingly broad cyberattack surface, cyberattack-resilient load forecasting for electric utilities is both more necessary and more challenging than ever. In th…

stat.AP2019★ 19 cited

A Bayesian marked spatial point processes model for basketball shot chart

Jieying Jiao, Guanyu Hu, Jun Yan

The success rate of a basketball shot may be higher at locations where a player makes more shots. For a marked spatial point process, this means that the mark and the intensity are…