Flexible Bayesian Regression Modeling
暫譯: 靈活的貝葉斯迴歸模型

Flexible Bayesian Regression Modeling is a step-by-step guide to the Bayesian revolution in regression modeling that can be used in advanced econometric and statistical analysis where datasets are characterized by complexity, multiplicity and large sample sizes. The book reviews three forms of flexibility, including methods which provide flexibility in their error distribution, methods which model non-central parts of the distribution (such as quantile regression), and models that allow the mean function to be flexible (such as spline models). Each chapter discusses the key aspects of fitting a regression model, including variable selection, identification of outliers, assumptions, informative output, and interpretation of results.

This book is particularly relevant to non-specialist practitioners with intermediate mathematical training who are seeking to apply Bayesian approaches in economics, biology and climate change.

• 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'