Building regression models with SAS : a guide for data scientists /

Building Regression Models with SAS: A Guide for Data Scientists teaches data scientists, statisticians, and other analysts who use SAS to train regression models for prediction with large, complex data. Each chapter focuses on a particular model and includes a high-level overview, followed by basic...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Rodriguez, Robert N. (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cary, NC : SAS Institute, 2023.
Edición:[First edition].
Temas:
SAS (Computer file)
Computer programming.
Programmation (Informatique)
computer programming.
Computer programming
Electronic books.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Intro
  • 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

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