Flexible Bayesian Regression Modelling

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  • Publisher : Academic Press
  • Release : 30 October 2019
  • ISBN : 9780128158630
  • Page : 302 pages
  • Rating : 4.5/5 from 103 voters

Flexible Bayesian Regression Modelling Book PDF summary

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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Flexible Bayesian Regression Modelling

Flexible Bayesian Regression Modelling
  • Author : Yanan Fan,David Nott,Mike Smith,Jean-Luc Dortet-Bernadet
  • Publisher : Academic Press
  • Release Date : 2019-10-30
  • ISBN : 9780128158630
DOWNLOAD 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 (

Data Analysis and Applications 1

Data Analysis and Applications 1
  • Author : Christos H. Skiadas,James R. Bozeman
  • Publisher : Wiley-ISTE
  • Release Date : 2019-04-02
  • ISBN : 9781786303820
DOWNLOAD BOOKData Analysis and Applications 1

This series of books collects a diverse array of work that provides the reader with theoretical and applied information on data analysis methods, models, and techniques, along with appropriate applications. Volume 1 begins with an introductory chapter by Gilbert Saporta, a leading expert in the field, who summarizes the developments in data analysis over the last 50 years. The book is then divided into three parts: Part 1 presents clustering and regression cases; Part 2 examines grouping and decomposition, GARCH and threshold models, structural

Applied Modeling Techniques and Data Analysis 2

Applied Modeling Techniques and Data Analysis 2
  • Author : Yannis Dimotikalis,Alex Karagrigoriou,Christina Parpoula,Christos H. Skiadas
  • Publisher : John Wiley & Sons
  • Release Date : 2021-05-11
  • ISBN : 9781786306746
DOWNLOAD BOOKApplied Modeling Techniques and Data Analysis 2

BIG DATA, ARTIFICIAL INTELLIGENCE AND DATA ANALYSIS SET Coordinated by Jacques Janssen Data analysis is a scientific field that continues to grow enormously, most notably over the last few decades, following rapid growth within the tech industry, as well as the wide applicability of computational techniques alongside new advances in analytic tools. Modeling enables data analysts to identify relationships, make predictions, and to understand, interpret and visualize the extracted information more strategically. This book includes the most recent advances on

Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling

Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling
  • Author : Ivan Jeliazkov,Justin Tobias
  • Publisher : Emerald Group Publishing
  • Release Date : 2019-10-18
  • ISBN : 9781838674212
DOWNLOAD BOOKTopics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling

Volume 40B of Advances in Econometrics examines innovations in stochastic frontier analysis, nonparametric and semiparametric modeling and estimation, A/B experiments, big-data analysis, and quantile regression.

A Bayesian Partial Identification Approach to Inferring the Prevalance of Accounting Misconduct

A Bayesian Partial Identification Approach to Inferring the Prevalance of Accounting Misconduct
  • Author : P. Richard Hahn
  • Publisher : Unknown
  • Release Date : 2016
  • ISBN : OCLC:1306220960
DOWNLOAD BOOKA Bayesian Partial Identification Approach to Inferring the Prevalance of Accounting Misconduct

This paper describes the use of flexible Bayesian regression models for estimating a partially identified probability function. Our approach permits efficient sensitivity analysis concerning the posterior impact of priors on the partially identified component of the regression model. The new methodology is illustrated on an important problem where only partially observed data are available - inferring the prevalence of accounting misconduct among publicly traded U.S. businesses.

The Oxford Handbook of Applied Bayesian Analysis

The Oxford Handbook of Applied Bayesian Analysis
  • Author : Anthony O' Hagan,Mike West
  • Publisher : OUP Oxford
  • Release Date : 2010-03-18
  • ISBN : 9780191582820
DOWNLOAD 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

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 Date : 1999-08-12
  • ISBN : 0198504853
DOWNLOAD 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.

Statistical Modelling and Regression Structures

Statistical Modelling and Regression Structures
  • Author : Thomas Kneib,Gerhard Tutz
  • Publisher : Springer Science & Business Media
  • Release Date : 2010-01-12
  • ISBN : 9783790824131
DOWNLOAD BOOKStatistical Modelling and Regression Structures

The contributions collected in this book have been written by well-known statisticians to acknowledge Ludwig Fahrmeir's far-reaching impact on Statistics as a science, while celebrating his 65th birthday. The contributions cover broad areas of contemporary statistical model building, including semiparametric and geoadditive regression, Bayesian inference in complex regression models, time series modelling, statistical regularization, graphical models and stochastic volatility models.