Flexible Bayesian Regression Modeling

Flexible Bayesian Regression Modeling is a step-by-step guide to the Bayesian revolution in regression modeling, for use in advanced econometric and statistical analysis where datasets are characterized by complexity, multiplicity, and large sample sizes, necessitating the need for considerable flexibility in modeling techniques. It reviews three forms of flexibility: methods which provide flexibility in their error distribution; methods which model non-central parts of the distribution (such as quantile regression); and finally models that allow the mean function to be flexible (such as spline models). Each chapter discusses the key aspects of fitting a regression model. R programs accompany the methods.

This book is particularly relevant to non-specialist practitioners with intermediate mathematical training seeking to apply Bayesian approaches in economics, biology, finance, engineering and medicine.

Key Features

Introduces powerful new nonparametric Bayesian regression techniques to classically trained practitioners

Focuses on approaches offering both superior power and methodological flexibility

Supplemented with instructive and relevant R programs within the text

Covers linear regression, nonlinear regression and quantile regression techniques

Provides diverse disciplinary case studies for correlation and optimization problems drawn from Bayesian analysis "in the wild"