Skip to main content

Flexible Bayesian Regression Modelling

  • 1st Edition - October 30, 2019
  • Editors: Yanan Fan, David Nott, Mike S. Smith, Jean-Luc Dortet-Bernadet
  • Language: English
  • Paperback ISBN:
    9 7 8 - 0 - 1 2 - 8 1 5 8 6 2 - 3
  • eBook ISBN:
    9 7 8 - 0 - 1 2 - 8 1 5 8 6 3 - 0

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 dat… Read more

Flexible Bayesian Regression Modelling

Purchase options

Limited Offer

Save 50% on book bundles

Immediately download your ebook while waiting for your print delivery. No promo code is needed.

Book bundle cover eBook and print

Institutional subscription on ScienceDirect

Request a sales quote

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.