Bayesian and frequentist regression methods /

Bayesian and frequentist regression methods / Jon Wakefield.

Bayesian and Frequentist Regression Methods provides a modern account of both Bayesian and frequentist methods of regression analysis. Many texts cover one or the other of the approaches, but this is the most comprehensive combination of Bayesian and frequentist methods that exists in one place. The...

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Bibliographic Details
Online Access: Full Text (via Springer)
Main Author: Wakefield, Jon
Format: eBook
Language:English
Published: New York, NY : Springer, ©2013.
Series:Springer series in statistics.
Subjects:
Regression analysis > Mathematical models.
Bayesian statistical decision theory.
Table of Contents:
  • Inferential Approaches
  • Frequentist Inference
  • Bayesian Inference
  • Hypothesis Testing and Variable Selection
  • Independent Data
  • Linear Models
  • General Regression Models
  • Binary Data Models
  • Dependent Data
  • Linear Models
  • General Regression Models
  • Nonparametric Modeling
  • Preliminaries for Nonparametric Regression
  • Spline and Kernel Methods
  • Nonparametric Regression with Multiple Predictors
  • Appendices
  • Differentiation of Matrix Expressions
  • Matrix Results
  • Some Linear Algebra
  • Probability Distributions and Generating Functions
  • Functions of Normal Random Variables
  • Some Results from Classical Statistics
  • Basic Large Sample Theory.

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