Filter Results:
(6)
Show Results For
- All HBS Web
(6)
- Research (6)
- Faculty Publications (5)
Show Results For
- All HBS Web
(6)
- Research (6)
- Faculty Publications (5)
Page 1 of 6
Results
- Article
Fast Subset Scan for Multivariate Spatial Biosurveillance
By: Daniel B. Neill, Edward McFowland III and Huanian Zheng
We present new subset scan methods for multivariate event detection in massive space-time datasets. We extend the recently proposed 'fast subset scan' framework from univariate to multivariate data, enabling computationally efficient detection of irregular space-time... View Details
Neill, Daniel B., Edward McFowland III, and Huanian Zheng. "Fast Subset Scan for Multivariate Spatial Biosurveillance." Statistics in Medicine 32, no. 13 (June 15, 2013): 2185–2208.
- Article
Fast Subset Scan for Multivariate Spatial Biosurveillance
By: Daniel B. Neill, Edward McFowland III and Huanian Zheng
We extend the recently proposed ‘fast subset scan’ framework from univariate to multivariate data, enabling computationally efficient detection of irregular space-time clusters even when the numbers of spatial locations and data streams are large. These fast algorithms... View Details
- 2016
- Article
Penalized Fast Subset Scanning
By: Skyler Speakman, Sriram Somanchi, Edward McFowland III and Daniel B. Neill
We present the penalized fast subset scan (PFSS), a new and general framework for scalable and accurate pattern detection. PFSS enables exact and efficient identification of the most anomalous subsets of the data, as measured by a likelihood ratio scan statistic.... View Details
Keywords: Disease Surveillance; Likelihood Ratio Statistic; Pattern Detection; Scan Statistic; Mathematical Methods
Speakman, Skyler, Sriram Somanchi, Edward McFowland III, and Daniel B. Neill. "Penalized Fast Subset Scanning." Journal of Computational and Graphical Statistics 25, no. 2 (2016): 382–404. (Selected for “Best of JCGS” invited session by the journal’s editor in chief.)
- 2015
- Article
Scalable Detection of Anomalous Patterns With Connectivity Constraints
By: Skyler Speakman, Edward McFowland III and Daniel B. Neill
We present GraphScan, a novel method for detecting arbitrarily shaped connected clusters in graph or network data. Given a graph structure, data observed at each node, and a score function defining the anomalousness of a set of nodes, GraphScan can efficiently and... View Details
Speakman, Skyler, Edward McFowland III, and Daniel B. Neill. "Scalable Detection of Anomalous Patterns With Connectivity Constraints." Journal of Computational and Graphical Statistics 24, no. 4 (2015): 1014–1033.
- 2011
- Article
Scalable Detection of Anomalous Patterns With Connectivity Constraints
By: Skyler Speakman, Edward McFowland III and Daniel B. Neill
We present GraphScan, a novel method for detecting arbitrarily shaped connected clusters in graph or network data. Given a graph structure, data observed at each node, and a score function defining the anomalousness of a set of nodes, GraphScan can efficiently and... View Details
- 16 Jul 2020
- Research & Ideas
Restaurant Revolution: How the Industry Is Fighting to Stay Alive
policies, frequent and more rigorous sanitation of all surfaces, touch-free interactions between customers and waitstaff, scanning QR codes, single-use menus or contactless, mobile-device ordering and payment, waitstaff screening and... View Details