Blending theory and practice, this will be the standard resource for statisticians and applied researchers. Assuming only basic knowledge of (non-measure-theoretic) probability and statistical inference, it is accessible to the wide range of researchers who use statistical modelling techniques. The author's complementary R package helps readers put theory into practice.
Blending theory and practice, this will be the standard resource for statisticians and applied researchers. Assuming only basic knowledge of (non-measure-theoretic) probability and statistical inference, it is accessible to the wide range of researchers who use statistical modelling techniques. The author's complementary R package helps readers put theory into practice.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Adelchi Azzalini was Professor of Statistics in the Department of Statistical Sciences at Università degli Studi di Padova, Italy until his retirement in 2013. Over the last fifteen years or so, much of his work has been dedicated to the research area of this book. He is regarded as the pioneer of this subject due to his 1985 paper on the skew-normal distribution; in addition, several of his subsequent papers, some of which have been written jointly with Antonella Capitanio, are considered to represent fundamental steps. He is the author or co-author of three books, over seventy research papers and four packages written in the R language.
Inhaltsangabe
Preface 1. Modulation of symmetric densities 2. The skew-normal distribution: probability 3. The skew-normal distribution: statistics 4. Heavy and adaptive tails 5. The multivariate skew-normal distribution 6. Skew-elliptical distributions 7. Further extensions and other directions 8. Application-oriented work Appendices References.
Preface 1. Modulation of symmetric densities 2. The skew-normal distribution: probability 3. The skew-normal distribution: statistics 4. Heavy and adaptive tails 5. The multivariate skew-normal distribution 6. Skew-elliptical distributions 7. Further extensions and other directions 8. Application-oriented work Appendices References.
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