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＜E-Book＞
Regression Modeling Strategies : With Applications to Linear Models, Logistic Regression, and Survival Analysis / by Frank E. Harrell
(Springer Series in Statistics. ISSN:2197568X)

Edition	1st ed. 2001.
Publisher	(New York, NY : Springer New York : Imprint: Springer)
Year	2001
Language	English
Size	XXIV, 572 p : online resource
Authors	*Harrell, Frank E author SpringerLink (Online service)
Subjects	LCSH:Probabilities LCSH:Statistics  LCSH:Biometry LCSH:Mathematical statistics -- Data processing  All Subject Search FREE:Probability Theory FREE:Statistical Theory and Methods FREE:Biostatistics FREE:Statistics and Computing
Notes	1 Introduction -- 2 General Aspects of Fitting Regression Models -- 3 Missing Data -- 4 Multivariable Modeling Strategies -- 5 Resampling, Validating, Describing, and Simplifying the Model -- 6 S-Plus Software -- 7 Case Study in Least Squares Fitting and Interpretation of a Linear Model -- 8 Case Study in Imputation and Data Reduction -- 9 Overview of Maximum Likelihood Estimation -- 10 Binary Logistic Regression -- 11 Logistic Model Case Study 1: Predicting Cause of Death -- 12 Logistic Model Case Study 2: Survival of Titanic Passengers -- 13 Ordinal Logistic Regression -- 14 Case Study in Ordinal Regression, Data Reduction, and Penalization -- 15 Models Using Nonparametric Transformations of X and Y -- 16 Introduction to Survival Analysis -- 17 Parametric Survival Models -- 18 Case Study in Parametric Survival Modeling and Model Approximation -- 19 Cox Proportional Hazards Regression Model -- 20 Case Study in Cox Regression Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about 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 dealing with 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. This text realistically deals with model uncertainty and its effects on inference to achieve "safe data mining" HTTP:URL=https://doi.org/10.1007/978-1-4757-3462-1

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