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.- 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'
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
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