Regression Modeling Strategies : With Applications to Linear Models, Logistic Regression, and Survival Analysis /
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 m...
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| Format: | eBook |
| Language: | English |
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New York, NY :
Springer New York,
2001.
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| Series: | Springer series in statistics.
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| Online Access: | Connect to the full text of this electronic book |
Table of Contents:
- 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 Regression, 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.