Results 1  10
of
38
A multivariate Bayesian scan statistic for early event detection and characterization
"... ..."
(Show Context)
Fast Subset Scan for Spatial Pattern Detection
 J. Royal Statistical Society B
"... Summary. We propose a new ‘fast subset scan ’ approach for accurate and computationally efficient event detection in massive data sets. We treat event detection as a search over subsets of data records, finding the subset which maximizes some score function. We prove that many commonly used function ..."
Abstract

Cited by 25 (9 self)
 Add to MetaCart
(Show Context)
Summary. We propose a new ‘fast subset scan ’ approach for accurate and computationally efficient event detection in massive data sets. We treat event detection as a search over subsets of data records, finding the subset which maximizes some score function. We prove that many commonly used functions (e.g. Kulldorff’s spatial scan statistic and extensions) satisfy the ‘linear time subset scanning ’ property, enabling exact and efficient optimization over subsets. In the spatial setting, we demonstrate that proximityconstrained subset scans substantially improve the timeliness and accuracy of event detection, detecting emerging outbreaks of disease 2 days faster than existing methods. Keywords: Algorithms; Disease surveillance; Event detection; Scan statistics; Spatial scan
Algorithms for εapproximation of terrains
, 2008
"... Consider a point set D with a measure functionµ: D→R. Let A be the set of subsets of D induced by containment in a shape from some geometric family (e.g. axisparallel rectangles, half planes, balls, koriented polygons). We say a range space (D, A) has anεapproximation P if ..."
Abstract

Cited by 10 (10 self)
 Add to MetaCart
Consider a point set D with a measure functionµ: D→R. Let A be the set of subsets of D induced by containment in a shape from some geometric family (e.g. axisparallel rectangles, half planes, balls, koriented polygons). We say a range space (D, A) has anεapproximation P if
Bayesian Network Scan Statistics for Multivariate Pattern Detection
"... We review three recently proposed scan statistic methods for multivariate pattern detection. Each method models the relationship between multiple observed and hidden variables using a Bayesian network structure, drawing inferences about the underlying pattern type and the affected subset of the da ..."
Abstract

Cited by 6 (1 self)
 Add to MetaCart
We review three recently proposed scan statistic methods for multivariate pattern detection. Each method models the relationship between multiple observed and hidden variables using a Bayesian network structure, drawing inferences about the underlying pattern type and the affected subset of the data. We first discuss the multivariate Bayesian scan statistic (MBSS) proposed by Neill and Cooper (2008). MBSS is a streambased event surveillance framework that detects and characterizes events given the aggregate counts for multiple data streams. Next, we describe the agentbased Bayesian scan statistic (ABSS) proposed by Jiang and Cooper (2008). ABSS performs event detection and characterization given individuallevel data for each agent in a population. Finally, we review the Anomalous Group Detection (AGD) method proposed by Das, Schneider, and Neill (2008). AGD is a general pattern detection approach which learns a Bayesian network structure from data and detects anomalous groups of records.
Detection of spatial clustering with average likelihood ratio test statistics
 Annals of Statistics
, 2009
"... ar ..."
(Show Context)
Cluster Detection in Networks using Percolation
"... We consider the task of detecting a salient cluster in a sensor network, i.e., an undirected graph with a random variable attached to each node. Motivated by recent research in environmental statistics and the drive to compete with the reigning scan statistic, we explore alternatives based on the pe ..."
Abstract

Cited by 5 (1 self)
 Add to MetaCart
(Show Context)
We consider the task of detecting a salient cluster in a sensor network, i.e., an undirected graph with a random variable attached to each node. Motivated by recent research in environmental statistics and the drive to compete with the reigning scan statistic, we explore alternatives based on the percolative properties of the network. The first method is based on the size of the largest connected component after removing the nodes in the network whose value is lower than a given threshold. The second one is the upper level set scan test introduced by Patil and Taillie (2003). We establish their performance in an asymptotic decision theoretic framework where the network size increases. We make abundant use of percolation theory to derive our theoretical results and our theory is complemented with some numerical experiments.
Networks of polynomial pieces with application to the analysis of point clouds and images
, 2008
"... We consider Hölder smoothness classes of surfaces for which we construct piecewise polynomial approximation networks, which are graphs with polynomial pieces as nodes and edges between polynomial pieces that are in ‘good continuation ’ of each other. Little known to the community, a similar construc ..."
Abstract

Cited by 3 (2 self)
 Add to MetaCart
(Show Context)
We consider Hölder smoothness classes of surfaces for which we construct piecewise polynomial approximation networks, which are graphs with polynomial pieces as nodes and edges between polynomial pieces that are in ‘good continuation ’ of each other. Little known to the community, a similar construction was used by Kolmogorov and Tikhomirov in their proof of their celebrated entropy results for Hölder classes. We show how to use such networks in the context of detecting geometric objects buried in noise to approximate the scan statistic, yielding an optimization problem akin to the Traveling Salesman. In the same context, we describe an alternative approach based on computing the longest path in the network after appropriate thresholding. For the special case of curves, we also formalize the notion of ‘good continuation’ between beamlets in any dimension, obtaining more economical piecewise linear approximation networks for curves. We include some numerical experiments illustrating the use of the beamlet network in characterizing the filamentarity content of 3D datasets, and show that even a rudimentary notion of good continuity may bring substantial improvement.
SpatioTemporal Exceedance Locations and Confidence Regions
"... An exceedance region is the set of locations in a spatial domain where a process exceeds some threshold. Examples of exceedance regions include areas where ozone concentrations exceed safety standards, there is high risk for tornadoes or floods, or heavymetal levels are dangerously high. Identifyin ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
An exceedance region is the set of locations in a spatial domain where a process exceeds some threshold. Examples of exceedance regions include areas where ozone concentrations exceed safety standards, there is high risk for tornadoes or floods, or heavymetal levels are dangerously high. Identifying these regions in a spatial or spatiotemporal setting is an important responsibility in environmental monitoring. Exceedance regions are often estimated by finding the areas where predictions from a statistical model exceed some threshold. Even when estimation error is quantifiable at individual locations, the overall estimation error of the estimated exceedance region is still unknown. A method is presented for constructing a confidence region containing the true exceedance region of a spatiotemporal process at a certain time. The underlying latent process and any measurement error are assumed to be Gaussian. Conventional techniques are used to model the spatiotemporal data, and then conditional simulation is combined with hypothesis testing to create the desired confidence
Detecting Irregularly Shaped Significant Spatial and SpatioTemporal Clusters
"... Detecting significant overdensity or underdensity clusters in spatiotemporal data is critical for many realworld applications. Most existing approaches are designed to deal with regularly shaped clusters such as circular, elliptic and rectangular ones, but cannot work well on irregularly shaped cl ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
(Show Context)
Detecting significant overdensity or underdensity clusters in spatiotemporal data is critical for many realworld applications. Most existing approaches are designed to deal with regularly shaped clusters such as circular, elliptic and rectangular ones, but cannot work well on irregularly shaped clusters. In this paper, we propose GridScan, a gridbased approach for detecting irregularly shaped spatial clusters. In GridScan, a cluster is asymptotically described by a set of connected grid cells and is computed by a fast greedy regiongrowing algorithm with elaborating cluster merging in the process. The time complexity of GridScan is linear to the number of grids, making it scalable to very large datasets. A prospective spatiotemporal cluster detection approach, GridScanPro, is also proposed by extending GridScan. Experiments and a case study in the epidemic scenario demonstrate that our approaches greatly outperform existing ones in terms of accuracy, efficiency, and scalability. 1