Building Regression Models with SAS: A Guide for Data Scientists

Contents
	Motivation for the Book
	Audiences for the Book
	Knowledge Prerequisites for the Book
	Software Prerequisites for the Book
	What the Book Does Not Cover
	Acknowledgments
Introduction
	Model Building at the Crossroads of Machine Learning and Statistics
	Overview of Procedures for Building Regression Models
	Practical Benefits
	When Does Interpretability Matter?
	When Should You Use the Procedures in This Book?
	How to Read This Book
General Linear Models
	Building General Linear Models: Concepts
		Example: Predicting Network Activity
		Essential Aspects of Regression Model Building
		Notation and Terminology for General Linear Models
		Parameter Estimation
		The Bias-Variance Tradeoff for Prediction
		Model Flexibility and Degrees of Freedom
		Assessment and Minimization of Prediction Error
		Summary
	Building General Linear Models: Issues
		Problems with Data-Driven Model Selection
		Example: Simulation of Selection Bias
		Freedman's Paradox
		Summary
	Building General Linear Models: Methods
		Best-Subset Regression
		Sequential Selection Methods
		Shrinkage Methods
		Summary
	Building General Linear Models: Procedures
		Introduction to the GLMSELECT Procedure
			Specifying the Candidate Effects and the Selection Method
			Controlling the Selection Method
			Which Selection Methods and Criteria Should You Use?
			Comparing Selection Criteria
			Forced Inclusion of Model Effects
			Example: Predicting the Close Rate of Retail Stores
			Example: Building a Model with Forward Selection
			Example: Building a Model with the Lasso and SBC
			Example: Building a Model with the Lasso and Cross Validation
			Example: Building a Model with the Lasso and Validation Data
			Example: Building a Model with the Adaptive Lasso
			Example: Building a Model with the Group Lasso
		Using the REG Procedure for Best-Subset Regression
			Example: Finding the Best Model for Close Rate
			Example: Best-Subset Regression with Categorical Predictors
		Introduction to the REGSELECT Procedure
			Example: Defining a CAS Session and Loading Data
			Example: Differences from the GLMSELECT Procedure
			Example: Building a Model with Forward Swap Selection
		Using the Final Model to Score New Data
		Summary
	Building General Linear Models: Collinearity
		Example: Modeling the Effect of Air Pollution on Mortality
		Detecting Collinearity
		Dimension Reduction Using Variable Clustering
		Ridge Regression
		The Elastic Net Method
		Principal Components Regression
		Partial Least Squares Regression
		Conclusions for Air Pollution Example
		Summary
	Building General Linear Models: Model Averaging
		Approaches to Model Averaging
		Using the GLMSELECT Procedure for Model Averaging
			Bootstrap Model Averaging with Stepwise Regression
			Refitting to Build a Parsimonious Model
		Model Averaging with Akaike Weights
		Summary
Specialized Regression Models
	Building Quantile Regression Models
		What Is a Quantile?
		How Does Quantile Regression Compare With Ordinary Least Squares Regression?
		Fitting Fully Specified Quantile Regression Models
			Example: Predicting Quantiles for Customer Lifetime Value
			Example: Fitting a Quantile Process Model for Customer Lifetime Value
		Introduction to the QUANTSELECT Procedure
			Example: Building Quantile Regression Models for Close Rate
			Example: Building a Quantile Process Model for Close Rate
			Example: Ranking Store Performance with Conditional Distributions
		Introduction to the QTRSELECT Procedure
			Example: Building Quantile Regression Models for Close Rate
		Summary
	Building Logistic Regression Models
		Comparison of Procedures for Logistic Regression
		Basic Concepts of Binary Logistic Regression
		Introduction to the HPLOGISTIC Procedure
		Introduction to the LOGSELECT Procedure
		Summary of Procedure Features
	Building Generalized Linear Models
		Procedures for Generalized Linear Models
		Basic Concepts of Generalized Linear Models
		Introduction to the HPGENSELECT Procedure
		Introduction to the GENSELECT Procedure
		Summary of Procedure Features
	Building Generalized Additive Models
		Procedures for Generalized Additive Models
		Components of Generalized Additive Models
		Introduction to the GAMPL Procedure
		Introduction to the GAMMOD Procedure
		Introduction to the GAMSELECT Procedure
		Summary
	Building Proportional Hazards Models
		Concepts of Proportional Hazards Models
		Introduction to the PHSELECT Procedure
		Model Building with Discrete Time
		Summary
	Building Classification and Regression Trees
		Introduction to the HPSPLIT Procedure
		Introduction to the TREESPLIT Procedure
		Summary
	Building Adaptive Regression Models
		Introduction to the ADAPTIVEREG Procedure
		Summary
Appendices about Algorithms and Computational Methods
	Algorithms for Least Squares Estimation
		The QR Decomposition
		The Singular Value Decomposition
		The Sweep Algorithm
		The Gram-Schmidt Procedure
			Orthogonalization in Univariate Regression without an Intercept
			Orthogonalization in Univariate Regression with an Intercept
			Orthogonalization in Multiple Regression
	Least Squares Geometry
		Orthogonality of Predictions and Residuals
		The Hat Matrix as a Projection Matrix
	Akaike's Information Criterion
		Forms of Akaike's Criterion
		Motivation for Akaike's Criterion
	Maximum Likelihood Estimation for Generalized Linear Models
		Computational Algorithms
		Existence of Maximum Likelihood Estimates
		Approximate Computation of Information Criteria
	Distributions for Generalized Linear Models
		The Exponential Family
		Continuous Distributions in the Exponential Family
		Discrete Distributions in the Exponential Family
		Distributions Outside of the Exponential Family
	Spline Methods
		Basic Terminology
		The Knot Selection Problem
		Types of Splines
		Spline Functionality in Procedures
	Algorithms for Generalized Additive Models
		Additive Models
		The Backfitting Algorithm for Additive Models
		Local Scoring for Generalized Additive Models
			The IWLS Algorithm for Generalized Linear Models
			The Local Scoring Algorithm for Generalized Additive Models
		Penalized Likelihood for Generalized Additive Models
			Algorithms for Penalized Likelihood Estimation
			Model Evaluation Criteria
			Searching for the Optimal
			Effective Degrees of Freedom
		Methods for Selecting Generalized Additive Models
			The Boosting Method
			The Shrinkage Method
Appendices about Common Topics
	Methods for Scoring Data
		Types of Scoring Methods
		Internal Scoring Methods
		External Scoring Methods
		Summary
	Coding Schemes for Categorical Predictors
		Why Does Parameterization Matter?
		Specifying the Parameterization and Level Order
		Specifying Effect Splitting
		Useful Parameterizations
			GLM Parameterization
			Effect Parameterization
			Reference Parameterization
		Other Parameterizations
		Summary
	Essentials of ODS Graphics
		Managing the Display of Graphs and Tables
		Creating SAS Data Sets from Graphs and Tables
		Accessing Individual Graphs
		Specifying the Size and Resolution of Graphs
		Specifying the Style of Graphs and Tables
			Distinguishing Groups in Graphs
		Modifying Graphs by Editing Graph Templates
		Creating Graphs by Writing Graph Templates
		Creating Graphs with Statistical Graphics Procedures
	Modifying a Procedure Graph
		Example: Enhancing a Contour Plot
			Capturing the Data in the Procedure Plot
			Determining the Template Name
			Accessing and Displaying the Template
			Modifying the Template Code
			Creating an Annotation Data Set with City Information
		Summary
	Marginal Model Plots
		Example: Claim Rates for Mortgages
		The %Marginal Macro
Glossary
References
Subject Index
Syntax Index
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