000			02649nam a22002177a 4500
999			_c4118 _d4118
005			20221111142038.0
008			221111b ||||| |||| 00| 0 eng d
020			_a9783319330396
082			_a519.536 _bHAR
100			_aHarrell, Frank E. _99320
245			_aRegression modeling strategies: _bwith applications to linear models, logistic regression, and survival analysis
250			_a2nd
260			_bSpringer _aSwitzerland _c2015
300			_axxv, 582 p.
365			_aEURO _b74.99
520			_aAbout this book This highly anticipated second edition features new chapters and sections, 225 new references, and comprehensive R software. In keeping with the previous edition, this book is about the art and science of data analysis and predictive modeling, which entails choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for fitting nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. The reader will gain a keen understanding of predictive accuracy, and the harm of categorizing continuous predictors or outcomes. This text realistically deals with model uncertainty, and its effects on inference, to achieve "safe data mining." It also presents many graphical methods for communicating complex regression models to non-statisticians. Regression Modeling Strategies presents full-scale case studies of non-trivial datasets instead of over-simplified illustrations of each method. These case studies use freely available R functions that make the multiple imputation, model building, validation, and interpretation tasks described in the book relatively easy to do. Most of the methods in this text apply to all regression models, but special emphasis is given to multiple regression using generalized least squares for longitudinal data, the binary logistic model, models for ordinal responses, parametric survival regression models, and the Cox semiparametric survival model. A new emphasis is given to the robust analysis of continuous dependent variables using ordinal regression.
650			_aRegression analysis _92357
650			_aLinear models (Statistics) _96115
650			_aMathematical statistics _9837
942			_2ddc _cBK