Flexible Bayesian Regression Modeling

Flexible Bayesian Regression Modeling

Fan, Yanan
Nott, David
Smith, Mike S.
Dortet-Bernadet, Jean-Luc

103,95 €(IVA inc.)

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 practitionersFocuses on approaches offering both superior power and methodological flexibilitySupplemented with instructive and relevant R programs within the textCovers linear regression, nonlinear regression, and quantile regression techniques in one volumeProvides diverse disciplinary case studies for correlation and optimization problems drawn from Bayesian analysis 'in the wild' INDICE: 1. Section on mean/median (linear) regression with Bayesian nonparametric methods to model the error distributions. This include methods using 2. Section focusing on quantile regression with various approaches, this section will describe methods which are flexible about the error distribution as well as modelling the non-central parts of the distributions 3. Section on nonlinear regression, this section will include Bayesian methods which flexibly model the mean/quantile functions, for example Bayesian splines and related computation

  • ISBN: 978-0-12-815862-3
  • Editorial: Academic Press
  • Encuadernacion: Rústica
  • Páginas: 352
  • Fecha Publicación: 01/09/2019
  • Nº Volúmenes: 1
  • Idioma: Inglés