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’
Applied non-specialist practitioners with intermediate mathematical training seeking to apply advanced statistical analysis of probability distributions, typically based in econometrics, biology, and climate change. Graduate students and 1st year PhD students in these areas
1. Section on mean/median (linear) regression with Bayesian nonparametric methods to model the error distributions
2. Section focusing on quantile regression with various approaches
3. Section on nonlinear regression
- No. of pages:
- © Academic Press 2020
- 1st November 2019
- Academic Press
- Paperback ISBN:
Dr Yanan Fan is Associate Professor of statistics at the University of New South Wales, Sydney, Australia. Her research focuses on the development of efficient Bayesian computational methods, approximate inferences and nonparametric regression methods.
University of New South Wales, Sydney, Australia
Prof. David Nott is Associate Professor of Statistics at the National University of Singapore. His research focuses on Bayesian likelihood-free inference and other approximate inference methods, and on complex Bayesian nonparametric models.
National University of Singapore
Prof. Michael Stanley Smith is Professor of Management (Econometrics) at Melbourne Business School, University of Melbourne, as well as Honorary Professor of Business Analytics at the University of Sydney. Michael’s research is in developing Bayesian models and methods, and applying them to problems that arise in business, economics and elsewhere.
University of Melbourne, Australia
Jean-Luc Dortet-Bernadet is maître de conférences at the Université de Strasbourg, France, and member of the Institut de Recherche Mathématique Avancée (IRMA). His research focuses mainly on the development of some Bayesian methods, nonparametric methods and on the study of dependence.
Institut de Recherche Mathematique Avancee, France