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In the last years, many efforts have been devoted to the development of statistical models able to well represent the increasing complexity of the phenomena under study. But these improvements have not concerned in the same way the statistical modeling of circular data (i.e. data representing directions and expressed in radians) because of the difficulty in their managing. This book provides the tool to extend all the common statistical methods and models to circular data as well. In the first part of the book both the descriptive and probabilistic aspects of circular data are given. Then, the wrapping approach and the wrapped Normal distribution are deeply analyzed and an ad hoc inferential Bayesian procedure is provided. Finally, two applications of the measurement error model for circular data in a spatial and in a dynamic spatiotemporal context are presented. This book is suitable both as an introductory manual on circular data and as an advanced analysis tool for further developments in circular modeling. Moreover, offering notions of spatiotemporal modeling, it is useful for statisticians who deal with environmental data that, frequently, present a spatiotemporal structure.

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Clarissa Ferrari, Ph.D. in Statistical Methodology for the Scientific Research at University of Bologna, Italy. In 2009, research associate at the National Institute of Geophysics and Volcanology (INGV), Italy. Currently, research associate in Statistics at Department of Economics, University of Verona, Italy.

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