Scan clustering: A false discovery approach

Authors:
M. Perone Pacifico;C. Genovese;I. Verdinelli;L. Wasserman
Affiliations:
Carnegie Mellon University, USA and Università di Roma “La Sapienza”, Italy;Carnegie Mellon University, USA;Carnegie Mellon University, USA and Università di Roma “La Sapienza”, Italy;Carnegie Mellon University, USA
Venue:
Journal of Multivariate Analysis
Year:
2007

Citing 3
Cited 0

Error process indexed by bandwidth matrices in multivariate local linear smoothing

Journal of Multivariate Analysis
Cluster analysis: a further approach based on density estimation

Computational Statistics & Data Analysis
Clustering Algorithms

Clustering Algorithms

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Abstract

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.