000			02880nam a2200373 i 4500
001			CR9781139248891
003			UkCbUP
005			20240301142635.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			120220s2014||||enk o ||1 0|eng|d
020			_a9781139248891 (ebook)
020			_z9781107029279 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQA273.6 _b.A98 2014
082	0	0	_a519.2/4 _223
100	1		_aAzzalini, Adelchi, _eauthor.
245	1	4	_aThe skew-normal and related families / _cAdelchi Azzalini, Università degli Studi di Padova with the collaboration of Antonella Capitanio, Università di Bologna.
246	3		_aThe Skew-Normal & Related Families
264		1	_aCambridge : _bCambridge University Press, _c2014.
300			_a1 online resource (viii, 262 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aInstitute of Mathematical Statistics monographs ; _v3
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
505	8		_aMachine generated contents note: 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.
520			_aInterest 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.
650		0	_aDistribution (Probability theory)
700	1		_aCapitanio, Antonella, _d1964- _eauthor.
776	0	8	_iPrint version: _z9781107029279
830		0	_aInstitute of Mathematical Statistics monographs ; _v3.
856	4	0	_uhttps://doi.org/10.1017/CBO9781139248891
999			_c9152 _d9152