Flexible Bayesian Regression Modelling

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  • Author : Yanan Fan
  • Publisher : Academic Press
  • Pages : 302 pages
  • ISBN : 0128158638
  • Rating : /5 from reviews
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Download or Read online Flexible Bayesian Regression Modelling full in PDF, ePub and kindle. this book written by Yanan Fan and published by Academic Press which was released on 30 October 2019 with total page 302 pages. We cannot guarantee that Flexible Bayesian Regression Modelling book is available in the library, click Get Book button and read full online book in your kindle, tablet, IPAD, PC or mobile whenever and wherever You Like. 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’

Flexible Bayesian Regression Modelling

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

Flexible Bayesian Regression Modeling

Flexible Bayesian Regression Modeling
  • Author : Yanan Fan,David Nott,Jean-Luc Dortet-Bernadet,Mike S. Smith
  • Publisher : Academic Press
  • Release : 01 September 2019
GET THIS BOOK Flexible Bayesian Regression Modeling

Flexible Bayesian Regression Modeling is a step-by-step guide to the Bayesian revolution in regression modeling that can be used in advanced econometric and statistical analysis where datasets are characterized by complexity, multiplicity and large sample sizes. The book reviews three forms of flexibility, including methods which provide flexibility in their error distribution, methods which model non-central parts of the distribution (such as quantile regression), and models that allow the mean function to be flexible (such as spline models). Each chapter

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

Topics in Identification  Limited Dependent Variables  Partial Observability  Experimentation  and Flexible Modeling
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  • Publisher : Emerald Group Publishing
  • Release : 18 October 2019
GET THIS BOOK Topics in Identification Limited Dependent Variables Partial Observability Experimentation and Flexible Modeling

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  • Publisher : OUP Oxford
  • Release : 18 March 2010
GET THIS BOOK The 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

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  • Publisher : Oxford University Press
  • Release : 12 August 1999
GET THIS BOOK Bayesian 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.

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

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  • Publisher : Springer Science & Business Media
  • Release : 12 January 2010
GET THIS BOOK Statistical 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.

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Nonparametric Bayesian Inference in Biostatistics
  • Author : Riten Mitra,Peter Müller
  • Publisher : Springer
  • Release : 25 July 2015
GET THIS BOOK Nonparametric Bayesian Inference in Biostatistics

As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has

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  • Publisher : Elsevier
  • Release : 10 September 2016
GET THIS BOOK Cognitive 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

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

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  • Author : Andrew Gelman,John B. Carlin,Hal S. Stern,David B. Dunson,Aki Vehtari,Donald B. Rubin
  • Publisher : CRC Press
  • Release : 27 November 2013
GET THIS BOOK Bayesian Data Analysis Third Edition

Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout

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  • Author : Ngianga-Bakwin Kandala,Gebrenegus Ghilagaber
  • Publisher : Springer Science & Business Media
  • Release : 06 September 2013
GET THIS BOOK Advanced Techniques for Modelling Maternal and Child Health in Africa

This book presents both theoretical contributions and empirical applications of advanced statistical techniques including geo-additive models that link individual measures with area variables to account for spatial correlation; multilevel models that address the issue of clustering within family and household; multi-process models that account for interdependencies over life-course events and non-random utilization of health services; and flexible parametric alternatives to existing intensity models. These analytical techniques are illustrated mainly through modeling maternal and child health in the African context, using

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  • Publisher : BoD – Books on Demand
  • Release : 11 April 2011
GET THIS BOOK Artificial Neural Networks

This book covers 27 articles in the applications of artificial neural networks (ANN) in various disciplines which includes business, chemical technology, computing, engineering, environmental science, science and nanotechnology. They modeled the ANN with verification in different areas. They demonstrated that the ANN is very useful model and the ANN could be applied in problem solving and machine learning. This book is suitable for all professionals and scientists in understanding how ANN is applied in various areas.

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Applied Bayesian Modelling
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  • Publisher : John Wiley & Sons
  • Release : 25 June 2014
GET THIS BOOK Applied Bayesian Modelling

This book provides an accessible approach to Bayesian computing and data analysis, with an emphasis on the interpretation of real data sets. Following in the tradition of the successful first edition, this book aims to make a wide range of statistical modeling applications accessible using tested code that can be readily adapted to the reader's own applications. The second edition has been thoroughly reworked and updated to take account of advances in the field. A new set of worked examples

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  • Publisher : University of Chicago Press
  • Release : 22 May 2019
GET THIS BOOK The 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