Flexible Bayesian Regression Modelling

Erscheinungsjahr 2019
ISBN: 978-0-12-815862-3
Verlag: Elsevier Science Publishing Co Inc

Buch, Englisch, 302 Seiten, Format (B × H): 153 mm x 226 mm, Gewicht: 495 g

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.

Zielgruppe

<p>Applied non-specialist practitioners with intermediate mathematical training seeking to apply advanced statistical analysis of probability distributions, typically based in econometrics, biology, and climate change. Graduate students and 1<SUP>st</SUP> year PhD students in these areas. </p>

Autoren/Hrsg.

Fan, Yanan

Nott, David

Smith, Mike S.

Dortet-Bernadet, Jean-Luc

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Bayesianische Inferenz
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Wirtschaftsinformatik
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik

Inhaltsverzeichnis

1. Bayesian quantile regression with the asymmetric Laplace distribution
2. A vignette on model-based quantile regression: analysing excess zero response
3. Bayesian nonparametric density regression for ordinal responses
4. Bayesian nonparametric methods for financial and macroeconomic time series analysis
5. Bayesian mixed binary-continuous copula regression with an application to childhood undernutrition
6. Nonstandard flexible regression via variational Bayes
7. Scalable Bayesian variable selection regression models for count data
8. Bayesian spectral analysis regression
9. Flexible regression modelling under shape constraints

Über Autor(innen)

Nott, David
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.

Fan, Yanan
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.

Dortet-Bernadet, Jean-Luc
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.

Smith, Mike S.
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.