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Flexible Bayesian Regression Modelling

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Flexible Bayesian Regression Modelling

Flexible Bayesian Regression Modelling Book
Author : Yanan Fan,David Nott,Mike S. Smith,Jean-Luc Dortet-Bernadet
Publisher : Academic Press
Release : 2019-10-30
ISBN : 0128158638
Language : En, Es, Fr & De

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Book Description :

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’

Bayesian Statistics 9

Bayesian Statistics 9 Book
Author : José M. Bernardo,M. J. Bayarri,James O. Berger,A. P. Dawid,David Heckerman
Publisher : Oxford University Press
Release : 2011-10-06
ISBN : 0199694583
Language : En, Es, Fr & De

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Book Description :

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.

The Oxford Handbook of Applied Bayesian Analysis

The Oxford Handbook of Applied Bayesian Analysis Book
Author : Anthony O' Hagan,Mike West
Publisher : OUP Oxford
Release : 2010-03-18
ISBN : 0191613894
Language : En, Es, Fr & De

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Book Description :

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 scientific ease and natural application of Bayesian modelling, and present solutions to real, engaging, societally important and demanding problems. The chapters are grouped into five general areas: Biomedical & Health Sciences; Industry, Economics & Finance; Environment & Ecology; Policy, Political & Social Sciences; and Natural & Engineering Sciences, and Appendix material in each touches on key concepts, models, and techniques of the chapter that are also of broader pedagogic and applied interest.

Journal of the American Statistical Association

Journal of the American Statistical Association Book
Author : Anonim
Publisher : Unknown
Release : 2008
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Journal of the American Statistical Association book written by , available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Bayesian Methods for Nonlinear Classification and Regression

Bayesian Methods for Nonlinear Classification and Regression Book
Author : David G. T. Denison,Christopher C. Holmes,Bani K. Mallick,Adrian F. M. Smith
Publisher : John Wiley & Sons
Release : 2002-05-06
ISBN : 9780471490364
Language : En, Es, Fr & De

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Book Description :

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 nonlinear modelling and Bayesian methods. * Focuses on the problems of classification and regression using flexible, data-driven approaches. * Demonstrates how Bayesian ideas can be used to improve existing statistical methods. * Includes coverage of Bayesian additive models, decision trees, nearest-neighbour, wavelets, regression splines, and neural networks. * Emphasis is placed on sound implementation of nonlinear models. * Discusses medical, spatial, and economic applications. * Includes problems at the end of most of the chapters. * Supported by a web site featuring implementation code and data sets. Primarily of interest to researchers of nonlinear statistical modelling, the book will also be suitable for graduate students of statistics. The book will benefit researchers involved inregression and classification modelling from electrical engineering, economics, machine learning and computer science.

Flexible Bayesian Models for Medical Diagnostic Data

Flexible Bayesian Models for Medical Diagnostic Data Book
Author : Vanda Inácio de Carvalho,Miguel Brás de Carvalho,Wesley O. Johnson,Adam Branscum
Publisher : Chapman and Hall/CRC
Release : 2016-05-15
ISBN : 9781466580398
Language : En, Es, Fr & De

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Book Description :

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 sample size determination.

Statistical Theory and Method Abstracts

Statistical Theory and Method Abstracts Book
Author : Anonim
Publisher : Unknown
Release : 2001
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Statistical Theory and Method Abstracts book written by , available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Application of Gaussian Process Priors on Bayesian Regression

Application of Gaussian Process Priors on Bayesian Regression Book
Author : Abhishek Bishoyi
Publisher : Unknown
Release : 2017
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

This dissertation aims at introducing Gaussian process priors on the regression to capture features of dataset more adequately. Three different types of problems occur often in the regression. 1) For the dataset with missing covariates in the semiparametric regression, we utilize Gaussian process priors on the nonparametric component of the regression function to perform imputations of missing covariates. For the Bayesian inference of parameters, we specify objective priors on the Gaussian process parameters.Posteriorpropriety of the model under the objective priors is also demonstrated. 2) For modeling binary and ordinal data, we proposed a flexible nonparametric regression model that combines flexible power link function with a Gaussian process prior on the latent regression function. We develop an efficient sampling algorithm for posterior inference and prove the posterior consistency of the proposed model. 3) In the high dimensional dataset, the estimation of regression coefficients especially when the covariates are highly multicollinear is very challenging. Therefore, we develop a model by using structured spike an slab prior on regression coefficients. Prior information of similarity between covariates can be encoded into the covariance structure of Gaussian process which can be used to induce sparsity. Hyperparameters of the Gaussian process can be used to control different sparsity pattern. The superiority of the proposed model is demonstrated using various simulation studies and real data examples.

Bayesian Ideas and Data Analysis

Bayesian Ideas and Data Analysis Book
Author : Ronald Christensen,Wesley Johnson,Adam Branscum,Timothy E Hanson
Publisher : CRC Press
Release : 2011-07-07
ISBN : 1439803552
Language : En, Es, Fr & De

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Book Description :

Emphasizing the use of WinBUGS and R to analyze real data, Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians presents statistical tools to address scientific questions. It highlights foundational issues in statistics, the importance of making accurate predictions, and the need for scientists and statisticians to collaborate in analyzing data. The WinBUGS code provided offers a convenient platform to model and analyze a wide range of data. The first five chapters of the book contain core material that spans basic Bayesian ideas, calculations, and inference, including modeling one and two sample data from traditional sampling models. The text then covers Monte Carlo methods, such as Markov chain Monte Carlo (MCMC) simulation. After discussing linear structures in regression, it presents binomial regression, normal regression, analysis of variance, and Poisson regression, before extending these methods to handle correlated data. The authors also examine survival analysis and binary diagnostic testing. A complementary chapter on diagnostic testing for continuous outcomes is available on the book’s website. The last chapter on nonparametric inference explores density estimation and flexible regression modeling of mean functions. The appropriate statistical analysis of data involves a collaborative effort between scientists and statisticians. Exemplifying this approach, Bayesian Ideas and Data Analysis focuses on the necessary tools and concepts for modeling and analyzing scientific data. Data sets and codes are provided on a supplemental website.

Dissertation Abstracts International

Dissertation Abstracts International Book
Author : Anonim
Publisher : Unknown
Release : 2008
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Dissertation Abstracts International book written by , available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Computer Science and Statistics

Computer Science and Statistics Book
Author : Richard M. Heiberger,Marianne T. Martin
Publisher : Unknown
Release : 1987
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Computer Science and Statistics book written by Richard M. Heiberger,Marianne T. Martin, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Modeling in Medical Decision Making

Modeling in Medical Decision Making Book
Author : Giovanni Parmigiani
Publisher : Wiley-Blackwell
Release : 2002-03
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Medical decision making has evolved in recent years, as more complex problems are being faced and addressed based on increasingly large amounts of data. In parallel, advances in computing power have led to a host of new and powerful statistical tools to support decision making. Simulation-based Bayesian methods are especially promising, as they provide a unified framework for data collection, inference, and decision making. In addition, these methods are simple to implement and can help to address the most pressing practical and ethical concerns arising in medical decision making. * Provides an overview of the necessary methodological background, including Bayesian inference, Monte Carlo simulation, and utility theory. * Driven by three real applications, presented as extensively detailed case studies. * Case studies include simplified versions of the analysis, to approach complex modelling in stages. * Features coverage of meta-analysis, decision analysis, and comprehensive decision modeling. * Accessible to readers with only a basic statistical knowledge. Primarily aimed at students and practitioners of biostatistics, the book will also appeal to those working in statistics, medical informatics, evidence-based medicine, health economics, health service research and health policy.

Bayesian Statistics and Its Applications

Bayesian Statistics and Its Applications Book
Author : Satyanshu K. Upadhyay,Umesh Singh,Dipak Dey
Publisher : Anshan Pub
Release : 2007
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

In the last two decades, Bayesian Statistics has acquired immense importance and has penetrated almost every area including those where the application of statistics appeared to be a remote possibility. This volume provides both theoretical and practical insights into the subject with detailed up-to-date material on various aspects. It serves two important objectives - to offer a thorough background material for theoreticians and gives a variety of applications for applied statisticians and practitioners. Consisting of 33 chapters, it covers topics on biostatistics, econometrics, reliability, image analysis, Bayesian computation, neural networks, prior elicitation, objective Bayesian methodologies, role of randomisation in Bayesian analysis, spatial data analysis, nonparametrics and a lot more. The book will serve as an excellent reference work for updating knowledge and for developing new methodologies in a wide variety of areas. It will become an invaluable tool for statisticians and the practitioners of Bayesian paradigm.

Enhancements to the Data Mining Process

Enhancements to the Data Mining Process Book
Author : George H. John
Publisher : Unknown
Release : 1997
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Enhancements to the Data Mining Process book written by George H. John, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Mathematical Reviews

Mathematical Reviews Book
Author : Anonim
Publisher : Unknown
Release : 2000
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Mathematical Reviews book written by , available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Bulletin

Bulletin Book
Author : Anonim
Publisher : Unknown
Release : 1998
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Bulletin book written by , available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Computing Science and Statistics

Computing Science and Statistics Book
Author : Kenneth Berk,Linda Malone,Terence M. Mulligan
Publisher : Unknown
Release : 1989
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Computing Science and Statistics book written by Kenneth Berk,Linda Malone,Terence M. Mulligan, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Transforming Health Care Through Informatics

Transforming Health Care Through Informatics Book
Author : Nancy M. Lorenzi
Publisher : Unknown
Release : 1999
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Transforming Health Care Through Informatics book written by Nancy M. Lorenzi, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

The BUGS Book

The BUGS Book Book
Author : David Lunn,Chris Jackson,Nicky Best,Andrew Thomas,David Spiegelhalter
Publisher : CRC Press
Release : 2012-10-02
ISBN : 1584888490
Language : En, Es, Fr & De

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Book Description :

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 and a wide range of applications from various disciplines. The book introduces regression models, techniques for criticism and comparison, and a wide range of modelling issues before going into the vital area of hierarchical models, one of the most common applications of Bayesian methods. It deals with essentials of modelling without getting bogged down in complexity. The book emphasises model criticism, model comparison, sensitivity analysis to alternative priors, and thoughtful choice of prior distributions—all those aspects of the "art" of modelling that are easily overlooked in more theoretical expositions. More pragmatic than ideological, the authors systematically work through the large range of "tricks" that reveal the real power of the BUGS software, for example, dealing with missing data, censoring, grouped data, prediction, ranking, parameter constraints, and so on. Many of the examples are biostatistical, but they do not require domain knowledge and are generalisable to a wide range of other application areas. Full code and data for examples, exercises, and some solutions can be found on the book’s website.