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 (

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

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

Regression

Regression
  • Author : Ludwig Fahrmeir,Thomas Kneib,Stefan Lang,Brian Marx
  • Publisher : Springer Science & Business Media
  • Release : 09 May 2013
GET THIS BOOKRegression

The aim of this book is an applied and unified introduction into parametric, non- and semiparametric regression that closes the gap between theory and application. The most important models and methods in regression are presented on a solid formal basis, and their appropriate application is shown through many real data examples and case studies. Availability of (user-friendly) software has been a major criterion for the methods selected and presented. Thus, the book primarily targets an audience that includes students, teachers

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.

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 : 18 October 2019
GET THIS 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.

Cognitive Computing: Theory and Applications

Cognitive Computing: Theory and Applications
  • Author : Vijay V Raghavan,Venkat N. Gudivada,Venu Govindaraju,C.R. Rao
  • Publisher : Elsevier
  • Release : 10 September 2016
GET THIS BOOKCognitive Computing: Theory and Applications

Cognitive Computing: Theory and Applications, written by internationally renowned experts, focuses on cognitive computing and its theory and applications, including the use of cognitive computing to manage renewable energy, the environment, and other scarce resources, machine learning models and algorithms, biometrics, Kernel Based Models for transductive learning, neural networks, graph analytics in cyber security, neural networks, data driven speech recognition, and analytical platforms to study the brain-computer interface. Comprehensively presents the various aspects of statistical methodology Discusses a wide variety

Data Analysis and Applications 1

Data Analysis and Applications 1
  • Author : Christos H. Skiadas,James R. Bozeman
  • Publisher : John Wiley & Sons
  • Release : 04 March 2019
GET THIS 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

The BUGS Book

The BUGS Book
  • Author : David Lunn,Chris Jackson,Nicky Best,Andrew Thomas,David Spiegelhalter
  • Publisher : CRC Press
  • Release : 02 October 2012
GET THIS BOOKThe BUGS Book

Bayesian statistical methods have become widely used for data analysis and modelling in recent years, and the BUGS software has become the most popular software for Bayesian analysis worldwide. Authored by the team that originally developed this software, The BUGS Book provides a practical introduction to this program and its use. The text presents complete coverage of all the functionalities of BUGS, including prediction, missing data, model criticism, and prior sensitivity. It also features a large number of worked examples

Bayesian Theory and Applications

Bayesian Theory and Applications
  • Author : Paul Damien,Petros Dellaportas,Nicholas G. Polson,David A. Stephens
  • Publisher : OUP Oxford
  • Release : 24 January 2013
GET THIS BOOKBayesian Theory and Applications

The development of hierarchical models and Markov chain Monte Carlo (MCMC) techniques forms one of the most profound advances in Bayesian analysis since the 1970s and provides the basis for advances in virtually all areas of applied and theoretical Bayesian statistics. This volume guides the reader along a statistical journey that begins with the basic structure of Bayesian theory, and then provides details on most of the past and present advances in this field. The book has a unique format.

Regression Modeling Strategies

Regression Modeling Strategies
  • Author : Frank E. Harrell
  • Publisher : Springer Science & Business Media
  • Release : 09 March 2013
GET THIS BOOKRegression Modeling Strategies

Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of

Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan

Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan
  • Author : Franzi Korner-Nievergelt,Tobias Roth,Stefanie von Felten,Jérôme Guélat,Bettina Almasi,Pius Korner-Nievergelt
  • Publisher : Academic Press
  • Release : 04 April 2015
GET THIS BOOKBayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan

Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN examines the Bayesian and frequentist methods of conducting data analyses. The book provides the theoretical background in an easy-to-understand approach, encouraging readers to examine the processes that generated their data. Including discussions of model selection, model checking, and multi-model inference, the book also uses effect plots that allow a natural interpretation of data. Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN introduces

The Economics of Artificial Intelligence

The Economics of Artificial Intelligence
  • Author : Ajay Agrawal,Joshua Gans,Avi Goldfarb
  • Publisher : University of Chicago Press
  • Release : 22 May 2019
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

Introduction to WinBUGS for Ecologists

Introduction to WinBUGS for Ecologists
  • Author : Marc Kery
  • Publisher : Academic Press
  • Release : 19 July 2010
GET THIS BOOKIntroduction to WinBUGS for Ecologists

Introduction to WinBUGS for Ecologists introduces applied Bayesian modeling to ecologists using the highly acclaimed, free WinBUGS software. It offers an understanding of statistical models as abstract representations of the various processes that give rise to a data set. Such an understanding is basic to the development of inference models tailored to specific sampling and ecological scenarios. The book begins by presenting the advantages of a Bayesian approach to statistics and introducing the WinBUGS software. It reviews the four most