Flexible Bayesian Regression Modelling (eBook, ePUB)

Redaktion: Fan, Yanan; Dortet-Bernadet, Jean-Luc; Smith, Mike S.; Nott, David

• Format: ePub

Produktbeschreibung

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'

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 302
• Erscheinungstermin: 30. Oktober 2019
• Englisch
• ISBN-13: 9780128158630
• Artikelnr.: 61689242

Autorenporträt

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.

Inhaltsangabe

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

Rezensionen

"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