Michael Matheny

activity

20182026

most citedPractical Low-Dimensional Halfspace Range Space Sampling

2 citations · 2 across the 3 of their papers we have counts for

collaborators

6 papers

stat.AP2026

Sampling for Region-Aggregated Spatial Scan Statistics

Foad Namjoo, Drew McClelland, Michael Matheny +1

Anomaly detection in geospatial data is a crucial tool in geographic information science (GIS), with applications ranging from national security to public-health surveillance to th…

cs.CG2021

Approximate Maximum Halfspace Discrepancy

Michael Matheny, Jeff M. Phillips

Consider the geometric range space $(X, H_{d})$ where $X \subset R^{d}$ and $H_{d}$ is the set of ranges defined by $d$ -dimensional halfspaces. In this setti…

cs.DS2019

Scalable Spatial Scan Statistics for Trajectories

Michael Matheny, Dong Xie, Jeff M. Phillips

We define several new models for how to define anomalous regions among enormous sets of trajectories. These are based on spatial scan statistics, and identify a geometric region wh…

stat.ML2019

The Kernel Spatial Scan Statistic

Mingxuan Han, Michael Matheny, Jeff M. Phillips

Kulldorff's (1997) seminal paper on spatial scan statistics (SSS) has led to many methods considering different regions of interest, different statistical models, and different app…

cs.CG2018★ 2 cited

Practical Low-Dimensional Halfspace Range Space Sampling

Michael Matheny, Jeff M. Phillips

We develop, analyze, implement, and compare new algorithms for creating $ε$ -samples of range spaces defined by halfspaces which have size sub-quadratic in $1/ ε$ …

cs.CG2018

Computing Approximate Statistical Discrepancy

Michael Matheny, Jeff M. Phillips

Consider a geometric range space $(X, c A)$ where each data point $x \in X$ has two or more values (say $r (x)$ and $b (x)$ ). Also consider a function $Φ (A)$ defined on any subset $A \…