Bøger af Joseph Glaz

Scan statistics is currently one of the most active and important areas of research in applied probability and statistics, having applications to a wide variety of fields: archaeology, astronomy, bioinformatics, biosurveillance, molecular biology, genetics, computer science, electrical engineering, geography, material sciences, physics, reconnaissance, reliability and quality control, telecommunication, and epidemiology.Filling a gap in the literature, this self-contained volume brings together a collection of selected chapters illustrating the depth and diversity of theory, methods and applications in the area of scan statistics. Key features:* Chapters are written by leading experts in the field.* Features many current results and highlights new directions for future research.* Includes challenging theoretical methodological research problems.* Presentation is accessible to statisticians as well as to scientists from other disciplines where scan statistics are employed.* Real-world applications to areas such as bioinformatics and biosurveillance are emphasized.* Contains extensive references to research articles, books, and relevant computer software.  Scan Statistics is an excellent reference for graduate students and researchers in applied probability and statistics, as well as for scientists in biology, computer science, pharmaceutical science, medicine, geography, quality control, communications, and epidemiology. The work may also be used as a textbook for a graduate-level seminar on scan statistics.

In this volume, we have brought together a collection of experts working in this area of research in order to review some of the developments that have taken place over the years and also to present their new works and point out some open problems.

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.