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Probability theory --- Probability distribution functions --- Normal density functions

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Interest in the skew-normal and related families of distributions has grown enormously over recent years, as theory has advanced, challenges of data have grown, and computational tools have made substantial progress. This comprehensive treatment, blending theory and practice, will be the standard resource for statisticians and applied researchers. Assuming only basic knowledge of (non-measure-theoretic) probability and statistical inference, the book is accessible to the wide range of researchers who use statistical modelling techniques. Guiding readers through the main concepts and results, it covers both the probability and the statistics sides of the subject, in the univariate and multivariate settings. The theoretical development is complemented by numerous illustrations and applications to a range of fields including quantitative finance, medical statistics, environmental risk studies, and industrial and business efficiency. The author's freely available R package sn, available from CRAN, equips readers to put the methods into action with their own data.

Distribution (Probability theory) --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Mathematical Sciences --- Probability

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Depth measurement. --- Distribution functions. --- Networks. --- Photometers. --- Remote sensing. --- Size distribution. --- Water vapor.

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Mathematical optimization. --- Mathematical models. --- Probability distribution functions --- Experimental data --- Computerized simulation --- Computer programs --- Executive routines

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Applications programs (computers) --- Information systems. --- Matrices (mathematics) --- Prediction analysis techniques. --- Probability distribution functions.