Flexible Bayesian Regression Modeling

By: Nott, Smith, Fan

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Paperback | 31 October 2019 | Edition Number 1

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304 Pages

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22.86 x 15.24 x 1.63

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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, including variable selection, identification of outliers, assumptions, informative output, and the interpretation of results. R programs accompany each method. This book is particularly relevant to non-specialist practitioners with intermediate mathematical training 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 in one volume
Provides diverse disciplinary case studies for correlation and optimization problems drawn from Bayesian analysis 'in the wild'

Industry 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

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