This book summarizes the methods and concepts of Statistical Implicative Analysis (SIA), created by Régis Gras in the 1980s to study, in a new way, the behavioural responses of French pupils to mathematics tests. Using a multidimensional, non-symmetrical data analysis method, SIA crosses a set of subjects or objects with a set of variables. It effectively complements traditional correlational and psychometric methods. SIA, through its various extensions, is today presented as a broad Artificial Intelligence method aimed at extracting trends and possible causalities in the form of rules, from a set of variables. It is based on the unlikeliness of the existence of these relationships, i.e. on the relative weakness of their counter-examples compared to what chance alone would produce. It establishes a dual topological relationship between the set of subjects and the set of variables. Many applications of this approach, driving forces or crucibles for the development of SIA, have concerned and still concern various fields such as didactics, evaluation and assessment, psychology, sociology, medicine, biology, economics, art history, and others. Key Features: Presents the foundations and representations of SIA. Provides extensions of variable sets and subjects. Includes a bonus exercise.

Point process statistics is successfully used in fields such as material science, human epidemiology, social sciences, animal epidemiology, biology, and seismology. Its further application depends greatly on good software and instructive case studies that show the way to successful work. This book satisfies this need by a presentation of the spatstat package and many statistical examples.

Researchers, spatial statisticians and scientists from biology, geosciences, materials sciences and other fields will use this book as a helpful guide to the application of point process statistics. No other book presents so many well-founded point process case studies.

From the reviews:

"For those interested in analyzing their spatial data, the wide variatey of examples and approaches here give a good idea of the possibilities and suggest reasonable paths to explore." Michael Sherman for the Journal of the American Statistical Association, December 2006