Scan clustering: A false discovery approach

We present a method that scans a random field for localized clusters while controlling the fraction of false discoveries. We use a kernel density estimator as the test statistic and adjust for the bias in this estimator by a method we introduce in this paper. We also show how to combine information...

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Bibliographic Details
Published in:Journal of multivariate analysis 2007-08, Vol.98 (7), p.1441-1469
Main Authors: Perone Pacifico, M., Genovese, C., Verdinelli, I., Wasserman, L.
Format: Article
Language:English
Subjects:
Bandwidth selection
Bandwidths
Cluster analysis
Estimating techniques
Estimation bias
Exact sciences and technology
False discovery proportion
False discovery proportion Multiple testing Kernel density estimators Bandwidth selection
Inference from stochastic processes
time series analysis
Kernel density estimators
Mathematics
Multiple testing
Multivariate analysis
Nonparametric inference
Probability and statistics
Probability theory and stochastic processes
Sciences and techniques of general use
Statistics
Stochastic processes
Studies
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Description
Summary:We present a method that scans a random field for localized clusters while controlling the fraction of false discoveries. We use a kernel density estimator as the test statistic and adjust for the bias in this estimator by a method we introduce in this paper. We also show how to combine information across multiple bandwidths while maintaining false discovery control.
ISSN:0047-259X
1095-7243
DOI:10.1016/j.jmva.2006.11.011