Regression modeling strategies : with applications to linear models, logistic regression, and survival analysis / Frank E. Harrell, Jr.
By: Harrell, Frank E.
Series: Springer series in statistics.Publisher: New York : Springer, 2015Edition: 2nd ed.Description: xxv, 582 p. : illustrations (some color) ; 27 cm.ISBN: 9783319194240.Subject(s): Regression analysis  Linear models (Statistics)DDC classification: 000SA.06Item type  Current location  Call number  Status  Date due  Barcode  Item holds  

Books 
ISI Library, Kolkata

000SA.06 H296 (Browse shelf)  Available  136741 
Includes bibliographical references and index.
1. Introduction 
2. General Aspects of Fitting Regression Models 
3. Missing Data 
4. Multivariable Modeling Strategies 
5. Describing, Resampling, Validating and Simplifying the Model 
6. R Software 
7. Modeling Longitudinal Responses using Generalized Least Squares 
8. Case Study in Data Reduction 
9. Overview of Maximum Likelihood Estimation 
10. Binary Logistic Regression 
11. Binary Logistic Regression Case Study 1 
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. Regression Models for Continuous Y and Case Study in Ordinal Regression 
16. TransformBothSides Regression 
17. Introduction to Survival Analysis 
18. Parametric Survival Models 
19. Case Study in Parametric Survival Modeling and Model Approximation 
20. Cox Proportional Hazards Regression Model 
21. Case Study in Cox Regression 
A Datasets, R packages, and internet resources 
References 
Index.
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 nonstatisticians.
Regression Modeling Strategies presents fullscale case studies of nontrivial datasets instead of oversimplified 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 semi parametric survival model. A new emphasis is given to the robust analysis of continuous dependent variables using ordinal regression.
Other editions of this work
Regression modeling strategies by Harrell Frank E 
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