Flexible Bayesian Regression Modelling - Yanan Fan,David Nott,Mike S. Smith,Jean-Luc Dortet-Bernadet
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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 th ... Full description
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Description
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.
More Information
| Author | Yanan Fan, David Nott, Mike S. Smith, Jean-Luc Dortet-Bernadet |
|---|---|
| Publisher | Elsevier Science |
| Release year | 2019 |
| Cover type | Softcover |
| EAN | 9780128158623 |