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Research on Probability Models for Cluster of Points Before the Year 1960
Scan statistics describe large number of events or objects clustered close in time or space. A few special cases of scan statistics – long success...
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Approximating the Distribution of the Multiple Scan Statistic
In this paper, we review a number of bounds and approximations for the distribution of the multiple scan statistic defined on a sequence of binary...
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Scan Statistics on Graphs and Networks
This article summarizes modern research of scan statistics on graphs and networks. These statistics arise naturally in the scanning of time and space...
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Spatial Cluster Estimation and Visualization Using Item Response Theory
In recent years Kulldorff’s circular scan statistic has become the most popular tool for detecting spatial clusters. However, window-imposed...
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Scan Statistics Viewed as Maximum of 1-Dependent Random Variables
A method of approximating the distribution function of the partial maximum sequence generated by a 1-dependent stationary sequence can be applied to...
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Scan Statistics for Detecting a Local Change in Mean for Normal Data
In this article, we review the approximations and inequalities that have been derived in the scientific literature for fixed-, multiple-, and...
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Variable Window Scan Statistics for Poisson Processes
We present methods to do fast online anomaly detection using scan statistics. Scan statistics have long been used to detect statistically significant...
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Waiting for Scans Containing Two Successes
In the present chapter, we present a review of results pertaining to the distribution of waiting times for the occurrence(s) of scans of type 2∕r in...
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Spatial Cluster Detection Through a Dynamic Programming Approach
This chapter reviews a dynamic programming scan approach to the detection and inference of arbitrarily shaped spatial clusters in aggregated...
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Spacing Methods and Their Applications to Scan Statistics
The scan statistics can be used in many areas of science to test for uniformity. In this chapter a review of spacing methods on scan statistic for...
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Nearest Neighbors of Multivariate Runs
We investigate the joint distributions of the number of nearest neighbor contacts between different objects in the context of runs-related statistics...
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New Frontiers for Scan Statistics: Network, Trajectory, and Text Data
In this chapter we survey the new theoretical developments and the use of scan statistics in data represented as graphs, trajectories, and text....
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Unitary Measures
This chapter considers unitary measures of test outcome which can be derived from the 2 × 2 contingency tableContingency table. In different...
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Other Measures, Other Tables
This chapter considers other measures which may be relevant to 2 × 2 contingency tablesContingency table. Firstly, methods to combine test results...
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A Comparison of Extreme Gradient and Gaussian Process Boosting for a Spatial Logistic Regression on Satellite Data
A popular and successful method of obtaining regression models using decision tree learners is XGBoost. However, the method implicitly assumes...
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Monitoring Viral Infections in Severe Acute Respiratory Syndrome Patients in Brazil
We introduce a novel methodology for estimating the distribution of viruses in Severe Acute Respiratory Syndrome (SARS) patients in Brazil,...
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Modelling of Overdispersed Count Rates
This paper revisits the common problem of analysing counts recorded over time through the modelling of the underlying rate, motivated by the analysis...
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A Computationally Efficient Spatio-Temporal Fusion Model for Reflectance Data
Fusing remotely-sensed reflectance data from different sources at different spatial and temporal scales is useful to monitor lake water quality. The...
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Sparse Intrinsic Gaussian Processes for Prediction on Manifolds: Extending Applications to Environmental Contexts
Traditional Gaussian Processes are limited in their application by complex boundaries and intricately structured manifolds, such as when predicting...
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A Distance-Based Statistic for Goodness-of-Fit Assessment
The modelling of count data in real world scenarios often requires models that address over-dispersion. Within the generalized linear modeling...