In many statistical applications, scientists have to analyze the occurrence of observed clusters of events in time or space. Scientists are especially interested in determining whether an observed cluster of events has occurred by chance if it is assumed that the events are distributed independently and uniformly over time or space. Scan statistics have relevant applications in many areas of science and technology including geology, geography, medicine, minefield detection, molecular biology, photography, quality control and reliability theory and radio-optics.

Introduction * Retrospective Scanning of Events over Time * Prospective Scanning of Events over Time * Success Scans in a Sequence of Trials * Higher Dimensional Scans * Scan Statistics in DNA Analysis * Approaches Used for derivations and approximations * Scanning N Uniform Distributed Points: Bounds * Approximations for the Conditional Case * Scanning Points in a Poisson Process * The Generalized Birthday Problem * Scan Statistics for a Sequence of Discrete i.i.d. Variables * Power * Testing for Clustering Superimposed on a Non-uniform Density * Multi-dimensional Case * Number of Clusters: Ordered Spacings * Extensions of the Scan Statistic