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001			285048
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007			cr nn 008mamaa
008			150903s2010 xxu| o |||| 0|eng d
020			_a9781441971708 _99781441971708
024	7		_a10.1007/9781441971708 _2doi
035			_avtls000338870
039		9	_a201509030814 _bVLOAD _c201404300351 _dVLOAD _y201402060921 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQA276-280
100	1		_aAndersen, Per Kragh. _eautor _944712
245	1	0	_aRegression with Linear Predictors / _cby Per Kragh Andersen, Lene Theil Skovgaard.
264		1	_aNew York, NY : _bSpringer New York, _c2010.
300			_aIx, 494 páginas _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 ; _v0
500			_aSpringer eBooks
505	0		_aStatistical models -- One categorical covariate -- One quantitative covariate -- Multiple regression, the linear predictor -- Model building: From purpose to conclusion -- Alternative outcome types and link functions -- Further topics.
520			_aThis text provides, in a non-technical language, a unified treatment of regression models for different outcome types, such as linear regression, logistic regression, and Cox regression. This is done by focusing on the many common aspects of these models, in particular the linear predictor, which combines the effects of all explanatory variables into a function which is linear in the unknown parameters. Specification and interpretation of various choices of parametrization of the effects of the covariates (categorical as well as quantitative) and interaction among these are elaborated upon. The merits and drawbacks of different link functions relating the linear predictor to the outcome are discussed with an emphasis on interpretational issues, and the fact that different research questions arise from adding or deleting covariates from the model is emphasized in both theory and practice. Regression models with a linear predictor are commonly used in fields such as clinical medicine, epidemiology, and public health, and the book, including its many worked examples, builds on the authors' more than thirty years of experience as teachers, researchers and consultants at a biostatistical department. The book is well-suited for readers without a solid mathematical background and is accompanied by Web pages documenting in R, SAS, and STATA, the analyses presented throughout the text. The authors are since 1978 affiliated with the Department of Biostatistics, University of Copenhagen. Per Kragh Andersen is professor; he is a co-author of the Springer book "Statistical Models Based on Counting Processes," and has served on editorial boards on several statistical journals. Lene Theil Skovgaard is associate professor; she has considerable experience as teacher and consultant, and has served on the editorial board of Biometrics.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aSkovgaard, Lene Theil. _eautor _9313128
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9781441971692
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-4419-7170-8 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c285048 _d285048