Online Public Access Catalogue (OPAC)
Library,Documentation and Information Science Division

“A research journal serves that narrow

borderland which separates the known from the unknown”

-P.C.Mahalanobis


Normal view MARC view ISBD view

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.06
Contents:
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. Transform-Both-Sides 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.
Summary: 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 semi parametric survival model. A new emphasis is given to the robust analysis of continuous dependent variables using ordinal regression.
Tags from this library: No tags from this library for this title. Log in to add tags.
Item type Current location Call number Status Date due Barcode Item holds
Books Books ISI Library, Kolkata
 
000SA.06 H296 (Browse shelf) Available 136741
Total holds: 0

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. Transform-Both-Sides 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 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 semi parametric survival model. A new emphasis is given to the robust analysis of continuous dependent variables using ordinal regression.

There are no comments for this item.

Log in to your account to post a comment.

Other editions of this work

Regression modeling strategies by Harrell Frank E
Library, Documentation and Information Science Division, Indian Statistical Institute, 203 B T Road, Kolkata 700108, INDIA
Phone no. 91-33-2575 2100, Fax no. 91-33-2578 1412, ksatpathy@isical.ac.in


Visitor Counter