000			02147nam a22003378i 4500
001			CR9780511811852
003			UkCbUP
005			20200124160246.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			101021s2007||||enk o ||1 0|eng|d
020			_a9780511811852 (ebook)
020			_z9780521857727 (hardback)
020			_z9780521674447 (paperback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQA161.B5 _bH55 2007
082	0	0	_a519.24 _222
100	1		_aHilbe, Joseph M., _d1944- _eauthor.
245	1	0	_aNegative binomial regression / _cJoseph M. Hilbe.
264		1	_aCambridge : _bCambridge University Press, _c2007.
300			_a1 online resource (xii, 251 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
520			_aAt last - a book devoted to the negative binomial model and its many variations. Every model currently offered in commercial statistical software packages is discussed in detail - how each is derived, how each resolves a distributional problem, and numerous examples of their application. Many have never before been thoroughly examined in a text on count response models: the canonical negative binomial; the NB-P model, where the negative binomial exponent is itself parameterized; and negative binomial mixed models. As the models address violations of the distributional assumptions of the basic Poisson model, identifying and handling overdispersion is a unifying theme. For practising researchers and statisticians who need to update their knowledge of Poisson and negative binomial models, the book provides a comprehensive overview of estimating methods and algorithms used to model counts, as well as specific guidelines on modeling strategy and how each model can be analyzed to access goodness-of-fit.
650		0	_aNegative binomial distribution.
650		0	_aPoisson algebras.
776	0	8	_iPrint version: _z9780521857727
856	4	0	_uhttps://doi.org/10.1017/CBO9780511811852
999			_c519005 _d519003