000			03957nam a22003735i 4500
001			279950
003			MX-SnUAN
005			20160429154004.0
007			cr nn 008mamaa
008			150903s2008 xxu| o |||| 0|eng d
020			_a9780387686394 _99780387686394
024	7		_a10.1007/9780387686394 _2doi
035			_avtls000331904
039		9	_a201509030208 _bVLOAD _c201404121949 _dVLOAD _c201404091716 _dVLOAD _y201402041015 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQA276-280
100	1		_aO'Quigley, John. _eautor _9304560
245	1	0	_aProportional Hazards Regression / _cby John O'Quigley.
264		1	_aNew York, NY : _bSpringer New York, _c2008.
300			_brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aStatistics for Biology and Health, _x1431-8776
500			_aSpringer eBooks
505	0		_aBackground: Probability -- Background: General inference -- Background: Survival analysis -- Marginal survival -- Regression models and subject heterogeneity -- Inference: Estimating equations -- Inference: Functions of Brownian motion -- Inference: Likelihood -- Inference: Stochastic integrals -- Inference: Small samples -- Inference: Changepoint models -- Explained variation -- Explained randomness -- Survival given covariates -- Proofs of theorems, lemmas and corollaries.
520			_aThe place in survival analysis now occupied by proportional hazards models and their generalizations is so large that it is no longer conceivable to offer a course on the subject without devoting at least half of the content to this topic alone. This book focuses on the theory and applications of a very broad class of models—proportional hazards and non-proportional hazards models, the former being viewed as a special case of the latter—which underlie modern survival analysis. Unlike other books in this area the emphasis is not on measure theoretic arguments for stochastic integrals and martingales. Instead, while inference based on counting processes and the theory of martingales is covered, much greater weight is placed on more traditional results such as the functional central limit theorem. This change in emphasis allows us in the book to devote much greater consideration to practical issues in modeling. The implications of different models, their practical interpretation, the predictive ability of any model, model construction, and model selection as well as the whole area of mis-specified models receive a great deal of attention. The book is aimed at both those interested in theory and those interested in applications. Many examples and illustrations are provided. The required mathematical and statistical background for those relatively new to the field is carefully outlined so that the material is accessible to a broad range of levels. John O’Quigley—Director of Research at the French Institut National de la Santé et de la Recherche Médicale and Professor of Mathematics at the University of California at San Diego—has published extensively on the subject of survival analysis, both in theoretical and applied journals. He has taught and carried out collaborative research at several of the world's leading departments of mathematics and statistics including the University of Washington, the Fred Hutchinson Cancer Research Center in Seattle, Harvard University, and Lancaster University, UK.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9780387251486
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-0-387-68639-4 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
942			_c14
999			_c279950 _d279950