000			03625nam a22004455i 4500
001			u371844
003			SIRSI
005			20160812080151.0
007			cr nn 008mamaa
008			100715s2010 xxu| s |||| 0|eng d
020			_a9781441971708 _9978-1-4419-7170-8
040			_cMX-MeUAM
050		4	_aQA276-280
082	0	4	_a519.5 _223
100	1		_aAndersen, Per Kragh. _eauthor.
245	1	0	_aRegression with Linear Predictors _h[recurso electrónico] / _cby Per Kragh Andersen, Lene Theil Skovgaard.
264		1	_aNew York, NY : _bSpringer New York, _c2010.
300			_aIX, 494p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aStatistics for Biology and Health, _x1431-8776 ; _v0
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.
650		0	_aStatistics.
650		0	_aMathematical statistics.
650	1	4	_aStatistics.
650	2	4	_aStatistical Theory and Methods.
650	2	4	_aStatistics for Life Sciences, Medicine, Health Sciences.
700	1		_aSkovgaard, Lene Theil. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781441971692
830		0	_aStatistics for Biology and Health, _x1431-8776 ; _v0
856	4	0	_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-1-4419-7170-8
596			_a19
942			_cLIBRO_ELEC
999			_c199724 _d199724