The skew-normal and related families 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. -- Complemented by the author’s freely available R package sn -- Suitable for newcomers to the field as well as specialists -- Written by two leading researchers in the area

References in zbMATH (referenced in 132 articles )

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  1. Amsler, Christine; Prokhorov, Artem; Schmidt, Peter: Endogeneity in stochastic frontier models (2016)
  2. Azzalini, Adelchi; Browne, Ryan P.; Genton, Marc G.; McNicholas, Paul D.: On nomenclature for, and the relative merits of, two formulations of skew distributions (2016)
  3. Boojari, Hossein; Khaledi, Majid Jafari; Rivaz, Firoozeh: A non-homogeneous skew-gaussian Bayesian spatial model (2016)
  4. Durio, A.; Nikitin, Ya.Yu.: Local efficiency of integrated goodness-of-fit tests under skew alternatives (2016)
  5. Dutta, Subhajit; Sarkar, Soham; Ghosh, Anil K.: Multi-scale classification using localized spatial depth (2016)
  6. Fung, Thomas; Seneta, Eugene: Tail asymptotics for the bivariate skew normal (2016)
  7. Harrar, Solomon W.; Xu, Jin: Confidence regions for level differences in growth curve models (2016)
  8. Jupp, P.E.; Regoli, G.; Azzalini, A.: A general setting for symmetric distributions and their relationship to general distributions (2016)
  9. Kahrari, F.; Rezaei, M.; Yousefzadeh, F.; Arellano-Valle, R.B.: On the multivariate skew-normal-Cauchy distribution (2016)
  10. Kim, Hyoung-Moon; Maadooliat, Mehdi; Arellano-Valle, Reinaldo B.; Genton, Marc G.: Skewed factor models using selection mechanisms (2016)
  11. Lee, Sharon X.; McLachlan, Geoffrey J.: Finite mixtures of canonical fundamental skew $t$-distributions. The unification of the restricted and unrestricted skew $t$-mixture models (2016)
  12. Lin, Tsung-I; McLachlan, Geoffrey J.; Lee, Sharon X.: Extending mixtures of factor models using the restricted multivariate skew-normal distribution (2016)
  13. Litvinova, V.V.; Nikitin, Ya.Yu.: Kolmogorov tests of normality based on some variants of Polya’s characterization (2016)
  14. Milošević, B.; Obradović, M.: Characterization based symmetry tests and their asymptotic efficiencies (2016)
  15. Young, Phil D.; Harvill, Jane L.; Young, Dean M.: A derivation of the multivariate singular skew-normal density function (2016)
  16. Zareifard, Hamid; Rue, Håvard; Khaledi, Majid Jafari; Lindgren, Finn: A skew Gaussian decomposable graphical model (2016)
  17. Abanto-Valle, C.A.; Lachos, V.H.; Dey, Dipak K.: Bayesian estimation of a skew-Student-$t$ stochastic volatility model (2015)
  18. Arevalillo, Jorge M.; Navarro, Hilario: A note on the direction maximizing skewness in multivariate skew-t vectors (2015)
  19. Arslan, Olcay: Variance-mean mixture of the multivariate skew normal distribution (2015)
  20. Belzunce, Félix; Mulero, Julio; Ruíz, José María; Suárez-Llorens, Alfonso: On relative skewness for multivariate distributions (2015)

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