Abstract
This chapter describes and summarizes methods for identifying the presence of clusters in a random field. The approach is based on controlling the fraction of false discoveries and considers a density estimator as the test statistic. A procedure called shaving is adopted for correcting the bias of the density estimator. This type of scanning for cluster identification does not use a window of fixed size; the role of the window size is played by the bandwidth of the kernel estimator. Clusters obtained using different bandwidths are combined in order to increase the detection power of the procedure. In this chapter we stress some more intuitive aspects of these procedures and present some applications.
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Perone-Pacifico, M., Verdinelli, I. (2009). False Discovery Control for Scan Clustering. In: Glaz, J., Pozdnyakov, V., Wallenstein, S. (eds) Scan Statistics. Statistics for Industry and Technology. Birkhäuser Boston. https://doi.org/10.1007/978-0-8176-4749-0_13
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DOI: https://doi.org/10.1007/978-0-8176-4749-0_13
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