Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis / Edition 1

Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis / Edition 1

by Jr., Frank Harrell Frank E.
     
 

ISBN-10: 0387952322

ISBN-13: 9780387952321

Pub. Date: 06/15/2001

Publisher: Springer New York

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…  See more details below

Overview

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".

Product Details

ISBN-13:
9780387952321
Publisher:
Springer New York
Publication date:
06/15/2001
Series:
Springer Series in Statistics
Edition description:
1st ed. 2001. Corr. 2nd printing 2006
Pages:
572
Sales rank:
1,313,085
Product dimensions:
1.31(w) x 10.00(h) x 7.00(d)

Table of Contents

Introduction
• General Aspects of Fitting Regression Models
• Missing Data
• Multivariable Modeling Strategies
• Resampling, Validating, Describing, and Simplifying the Model
• S-PLUS Software
• Case Study in Least Squares Fitting and Interpretation of a Linear Model
• Case Study in Imputation and Data Reduction
• Overview of Maximum Likelihood Estimation
• Binary Logistic Regression
• Logistic Model Case Study 1: Predicting Cause of Death
• Logistic Model Case Study 2: Survival of Titanic Passengers
• Ordinal Logistic Regression
• Case Study in Ordinal Regrssion, Data Reduction, and Penalization
• Models Using Nonparametic Transformations of X and Y
• Introduction to Survival Analysis
• Parametric Survival Models
• Case Study in Parametric Survival Modeling and Model Approximation
• Cox Proportional Hazards Regression Model
• Case Study in Cox Regression

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