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 Frank Harrell
     
 

ISBN-10: 1441929185

ISBN-13: 9781441929181

Pub. Date: 11/02/2010

Publisher: Springer New York

There are many books that are excellent sources of knowledge about individual stastical tools (survival models, general linear models, etc.), but the art of data analysis is about choosing and using multiple tools. In the words of Chatfield "...students typically know the technical details of regressin for example, but not necessarily when and how to apply it. This

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Overview

There are many books that are excellent sources of knowledge about individual stastical tools (survival models, general linear models, etc.), but the art of data analysis is about choosing and using multiple tools. In the words of Chatfield "...students typically know the technical details of regressin for example, but not necessarily when and how to apply it. This argues the need for a better balance in the literature and in statistical teaching between techniques and problem solving strategies." Whether analyzing risk factors, adjusting for biases in observational studies, or developing predictive models, there are common problems that few regression texts address. For example, there are missing data in the majority of datasets one is likely to encounter (other than those used in textbooks!) but most regression texts do not include methods for dealing with such data effectively, and texts on missing data do not cover regression modeling.

Product Details

ISBN-13:
9781441929181
Publisher:
Springer New York
Publication date:
11/02/2010
Series:
Springer Series in Statistics
Edition description:
Softcover reprint of hardcover 1st ed. 2001
Pages:
572
Product dimensions:
6.80(w) x 9.50(h) x 1.50(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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