Flexible Bayesian Regression Modelling

by Yanan Fan (Editor), David Nott (Editor), Mike S. Smith (Editor), Jean-Luc Dortet-Bernadet (Editor)

Overview

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. - 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'

Product Details

ISBN-13:	9780128158630
Publisher:	Elsevier Science & Technology Books
Publication date:	10/30/2019
Sold by:	Barnes & Noble
Format:	eBook
Pages:	302
File size:	41 MB
Note:	This product may take a few minutes to download.

About the Author

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.
Dr. 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.
Dr. 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.
Dr. 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.

Table of Contents

1. Bayesian quantile regression with the asymmetric Laplace distribution2. A vignette on model-based quantile regression: analysing excess zero response3. Bayesian nonparametric density regression for ordinal responses4. Bayesian nonparametric methods for financial and macroeconomic time series analysis5. Bayesian mixed binary-continuous copula regression with an application to childhood undernutrition6. Nonstandard flexible regression via variational Bayes7. Scalable Bayesian variable selection regression models for count data8. Bayesian spectral analysis regression9. Flexible regression modelling under shape constraints

What People are Saying About This

From the Publisher

Supports practitioners seeking to understand and implement the Bayesian regression standard of statistical probability informed by distributional credibility

Editorial Reviews

Flexible Bayesian Regression Modelling is a step-by-step guide to the Bayesian revolution in regression modelling, 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 modelling techniques." —Mathematical Reviews Clippings

From the Publisher