Flexible Bayesian Regression Modelling

Dr. Yanan Fan is Associate Professor of statistics at the University of New South Wales, Sydney, Australia. Her research focuses on the development of efficient Bayesian computational methods, approximate inferences and nonparametric regression methods. Dr. David Nott is Associate Professor of Statistics at the National University of Singapore. His research focuses on Bayesian likelihood-free inference and other approximate inference methods, and on complex Bayesian nonparametric models. Dr. Michael Stanley Smith is Professor of Management (Econometrics) at Melbourne Business School, University of Melbourne, as well as Honorary Professor of Business Analytics at the University of Sydney. Michael’s research is in developing Bayesian models and methods, and applying them to problems that arise in business, economics and elsewhere. Dr. Jean-Luc Dortet-Bernadet is maître de conférences at the Université de Strasbourg, France, and member of the Institut de Recherche Mathématique Avancée (IRMA). His research focuses mainly on the development of some Bayesian methods, nonparametric methods and on the study of dependence.

• 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

“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