Using scan statistics for cluster detection: recognizing real bandwagons

DOI10.1007/s11009-019-09737-1zbMath1457.62129OpenAlexW2964226825WikidataQ127436996 ScholiaQ127436996MaRDI QIDQ2218858

Jie Chen, Paul Jorgensen, Thomas S. Ferguson

Publication date: 18 January 2021

Published in: Methodology and Computing in Applied Probability (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11009-019-09737-1

zbMATH Keywords

likelihood ratio testscan statisticstesting homogeneitysimulation studiesbandwagonminimum \(p\)-value statisticsvariable window

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Clustering in the social and behavioral sciences (91C20) Applications of statistics to social sciences (62P25) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Uses Software

• bootlib

Cites Work

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• Scan statistics for detecting a local change in variance for normal data with unknown population variance
• Scan statistics for detecting a local change in variance for normal data with known variance
• A multiple window scan statistic for time series models
• Tight bounds and approximations for scan statistic probabilities for discrete data
• Approximation and simulation of the distributions of scan statistics for Poisson processes in higher dimensions
• A new method for estimating the distribution of scan statistics for a two-dimensional Poisson process
• Scan statistics for monitoring data modeled by a negative binomial distribution
• Variable Window Scan Statistics for Normal Data
• Multiple Window Discrete Scan Statistics
• Approximations for Distributions of Scan Statistics
• Large-deviation approximations to the distribution of scan statistics
• On the Distributions of Scan Statistics of a Two-Dimensional Poisson Process
• Some Probabilities, Expectations and Variances for the Size of Largest Clusters and Smallest Intervals
• Scan statistics

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