Flexible Bayesian Regression Modelling

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’

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  • Author : Yanan Fan
  • Publisher : Academic Press
  • Pages : 302 pages
  • ISBN : 0128158638
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKFlexible Bayesian Regression Modelling

Flexible Bayesian Regression Modelling

Flexible Bayesian Regression Modelling
  • Author : Yanan Fan,David Nott,Mike S. Smith,Jean-Luc Dortet-Bernadet
  • Publisher : Academic Press
  • Release : 30 October 2019
GET THIS BOOKFlexible Bayesian Regression Modelling

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 (

The Oxford Handbook of Applied Bayesian Analysis

The Oxford Handbook of Applied Bayesian Analysis
  • Author : Anthony O' Hagan,Mike West
  • Publisher : OUP Oxford
  • Release : 18 March 2010
GET THIS BOOKThe Oxford Handbook of Applied Bayesian Analysis

Bayesian analysis has developed rapidly in applications in the last two decades and research in Bayesian methods remains dynamic and fast-growing. Dramatic advances in modelling concepts and computational technologies now enable routine application of Bayesian analysis using increasingly realistic stochastic models, and this drives the adoption of Bayesian approaches in many areas of science, technology, commerce, and industry. This Handbook explores contemporary Bayesian analysis across a variety of application areas. Chapters written by leading exponents of applied Bayesian analysis showcase

The Economics of Artificial Intelligence

The Economics of Artificial Intelligence
  • Author : Ajay Agrawal,Joshua Gans,Avi Goldfarb
  • Publisher : National Bureau of Economic Re
  • Release : 24 February 2021
GET THIS BOOKThe Economics of Artificial Intelligence

Advances in artificial intelligence (AI) highlight the potential of this technology to affect productivity, growth, inequality, market power, innovation, and employment. This volume seeks to set the agenda for economic research on the impact of AI. It covers four broad themes: AI as a general purpose technology; the relationships between AI, growth, jobs, and inequality; regulatory responses to changes brought on by AI; and the effects of AI on the way economic research is conducted. It explores the economic influence

Bayesian Statistics 6

Bayesian Statistics 6
  • Author : José M. Bernardo,James O. Berger,A. P. Dawid,Adrian F. M. Smith
  • Publisher : Oxford University Press
  • Release : 12 August 1999
GET THIS BOOKBayesian Statistics 6

Bayesian statistics is a dynamic and fast-growing area of statistical research and the Valencia International Meetings provide the main forum for discussion. These resulting proceedings form an up-to-date collection of research.

Bayesian Methods for Nonlinear Classification and Regression

Bayesian Methods for Nonlinear Classification and Regression
  • Author : David G. T. Denison,Christopher C. Holmes,Bani K. Mallick,Adrian F. M. Smith
  • Publisher : John Wiley & Sons
  • Release : 06 May 2002
GET THIS BOOKBayesian Methods for Nonlinear Classification and Regression

Nonlinear Bayesian modelling is a relatively new field, but one that has seen a recent explosion of interest. Nonlinear models offer more flexibility than those with linear assumptions, and their implementation has now become much easier due to increases in computational power. Bayesian methods allow for the incorporation of prior information, allowing the user to make coherent inference. Bayesian Methods for Nonlinear Classification and Regression is the first book to bring together, in a consistent statistical framework, the ideas of

Bayesian Statistics 9

Bayesian Statistics 9
  • Author : José M. Bernardo,M. J. Bayarri,James O. Berger,A. P. Dawid,David Heckerman
  • Publisher : Oxford University Press
  • Release : 06 October 2011
GET THIS BOOKBayesian Statistics 9

Bayesian statistics is a dynamic and fast-growing area of statistical research and the Valencia International Meetings provide the main forum for discussion. These resulting proceedings form an up-to-date collection of research.

Flexible Bayesian Models for Medical Diagnostic Data

Flexible Bayesian Models for Medical Diagnostic Data
  • Author : Vanda Inácio de Carvalho,Miguel Brás de Carvalho,Wesley O. Johnson,Adam Branscum
  • Publisher : Chapman and Hall/CRC
  • Release : 15 May 2016
GET THIS BOOKFlexible Bayesian Models for Medical Diagnostic Data

Offering a detailed and careful explanation of the methods, this book delineates Bayesian non parametric techniques to be used in health care and the statistical evaluation of diagnostic tests to determine accuracy before mass use in practice. Unique to these methods is the incorporation of prior information and elimination of subjective beliefs and asymptotic results. It includes examples such as ROC curves and ROC surfaces estimation, modeling of multivariate diagnostic data, absence of a perfect test, ROC regression methodology, and

Bayesian Hierarchical Models

Bayesian Hierarchical Models
  • Author : Peter D. Congdon
  • Publisher : CRC Press
  • Release : 16 September 2019
GET THIS BOOKBayesian Hierarchical Models

An intermediate-level treatment of Bayesian hierarchical models and their applications, this book demonstrates the advantages of a Bayesian approach to data sets involving inferences for collections of related units or variables, and in methods where parameters can be treated as random collections. Through illustrative data analysis and attention to statistical computing, this book facilitates practical implementation of Bayesian hierarchical methods. The new edition is a revision of the book Applied Bayesian Hierarchical Methods. It maintains a focus on applied modelling

Bayesian Regression Modeling with INLA

Bayesian Regression Modeling with INLA
  • Author : Xiaofeng Wang,Yu Yue Ryan,Julian J. Faraway
  • Publisher : CRC Press
  • Release : 29 January 2018
GET THIS BOOKBayesian Regression Modeling with INLA

This book addresses the applications of extensively used regression models under a Bayesian framework. It emphasizes efficient Bayesian inference through integrated nested Laplace approximations (INLA) and real data analysis using R. The INLA method directly computes very accurate approximations to the posterior marginal distributions and is a promising alternative to Markov chain Monte Carlo (MCMC) algorithms, which come with a range of issues that impede practical use of Bayesian models.

Applied Econometrics with R

Applied Econometrics with R
  • Author : Christian Kleiber,Achim Zeileis
  • Publisher : Springer Science & Business Media
  • Release : 10 December 2008
GET THIS BOOKApplied Econometrics with R

R is a language and environment for data analysis and graphics. It may be considered an implementation of S, an award-winning language initially - veloped at Bell Laboratories since the late 1970s. The R project was initiated by Robert Gentleman and Ross Ihaka at the University of Auckland, New Zealand, in the early 1990s, and has been developed by an international team since mid-1997. Historically, econometricians have favored other computing environments, some of which have fallen by the wayside, and