Download Flexible Bayesian Regression Modelling Ebook PDF

Flexible Bayesian Regression Modelling

Flexible Bayesian Regression Modelling
A Book

by Yanan Fan,David Nott,Mike S. Smith,Jean-Luc Dortet-Bernadet

  • Publisher : Academic Press
  • Release : 2019-10-30
  • Pages : 302
  • ISBN : 0128158638
  • Language : En, Es, Fr & De
GET BOOK

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
A Book

by José M. Bernardo,M. J. Bayarri,James O. Berger,A. P. Dawid,David Heckerman

  • Publisher : Oxford University Press
  • Release : 2011-10-06
  • Pages : 706
  • ISBN : 0199694583
  • Language : En, Es, Fr & De
GET BOOK

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.

Journal of the American Statistical Association

Journal of the American Statistical Association
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2008
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
GET BOOK

Bayesian Statistics 6

Bayesian Statistics 6
Proceedings of the Sixth Valencia International Meeting

by José M. Bernardo,James O. Berger,A. P. Dawid,Adrian F. M. Smith

  • Publisher : Oxford University Press
  • Release : 1999-08-12
  • Pages : 867
  • ISBN : 9780198504856
  • Language : En, Es, Fr & De
GET BOOK

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
A Book

by David G. T. Denison,Christopher C. Holmes,Bani K. Mallick,Adrian F. M. Smith

  • Publisher : John Wiley & Sons
  • Release : 2002-05-06
  • Pages : 296
  • ISBN : 9780471490364
  • Language : En, Es, Fr & De
GET BOOK

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
A Book

by Vanda Inácio de Carvalho,Miguel Brás de Carvalho,Wesley O. Johnson,Adam Branscum

  • Publisher : Chapman and Hall/CRC
  • Release : 2016-05-15
  • Pages : 250
  • ISBN : 9781466580398
  • Language : En, Es, Fr & De
GET BOOK

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
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2001
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
GET BOOK

Bayesian Ideas and Data Analysis

Bayesian Ideas and Data Analysis
An Introduction for Scientists and Statisticians

by Ronald Christensen,Wesley Johnson,Adam Branscum,Timothy E Hanson

  • Publisher : CRC Press
  • Release : 2011-07-07
  • Pages : 516
  • ISBN : 1439803552
  • Language : En, Es, Fr & De
GET BOOK

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.

Application of Gaussian Process Priors on Bayesian Regression

Application of Gaussian Process Priors on Bayesian Regression
A Book

by Abhishek Bishoyi

  • Publisher : Unknown Publisher
  • Release : 2017
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
GET BOOK

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.

Dissertation Abstracts International

Dissertation Abstracts International
The sciences and engineering. B

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2008
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
GET BOOK

Computer Science and Statistics

Computer Science and Statistics
Proceedings of the 19th Symposium on the Interface, Philadelphia, Pennsylvania, March [8-11], 1987

by Richard M. Heiberger,Marianne T. Martin

  • Publisher : Unknown Publisher
  • Release : 1987
  • Pages : 588
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
GET BOOK

Modeling in Medical Decision Making

Modeling in Medical Decision Making
A Bayesian Approach

by Giovanni Parmigiani

  • Publisher : Wiley-Blackwell
  • Release : 2002-03
  • Pages : 266
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
GET BOOK

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
A Book

by Satyanshu K. Upadhyay,Umesh Singh,Dipak Dey

  • Publisher : Anshan Pub
  • Release : 2007
  • Pages : 507
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
GET BOOK

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
A Book

by George H. John

  • Publisher : Unknown Publisher
  • Release : 1997
  • Pages : 194
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
GET BOOK

Bayesian Econometric Methods

Bayesian Econometric Methods
A Book

by Joshua Chan,Gary Koop,Dale J. Poirier,Justin L. Tobias

  • Publisher : Cambridge University Press
  • Release : 2019-08-31
  • Pages : 480
  • ISBN : 1108423388
  • Language : En, Es, Fr & De
GET BOOK

Illustrates Bayesian theory and application through a series of exercises in question and answer format.

Bayesian Nonparametrics

Bayesian Nonparametrics
A Book

by Nils Lid Hjort,Chris Holmes,Peter Müller,Stephen G. Walker

  • Publisher : Cambridge University Press
  • Release : 2010-04-12
  • Pages : 329
  • ISBN : 1139484605
  • Language : En, Es, Fr & De
GET BOOK

Bayesian nonparametrics works - theoretically, computationally. The theory provides highly flexible models whose complexity grows appropriately with the amount of data. Computational issues, though challenging, are no longer intractable. All that is needed is an entry point: this intelligent book is the perfect guide to what can seem a forbidding landscape. Tutorial chapters by Ghosal, Lijoi and Prünster, Teh and Jordan, and Dunson advance from theory, to basic models and hierarchical modeling, to applications and implementation, particularly in computer science and biostatistics. These are complemented by companion chapters by the editors and Griffin and Quintana, providing additional models, examining computational issues, identifying future growth areas, and giving links to related topics. This coherent text gives ready access both to underlying principles and to state-of-the-art practice. Specific examples are drawn from information retrieval, NLP, machine vision, computational biology, biostatistics, and bioinformatics.

Mathematical Reviews

Mathematical Reviews
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2000
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
GET BOOK

Bulletin

Bulletin
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 1998
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
GET BOOK

Computing Science and Statistics

Computing Science and Statistics
Proceedings of the 21st Symposium on the Interface, Orlando, Florida, April 1989

by Kenneth Berk,Linda Malone,Terence M. Mulligan

  • Publisher : Unknown Publisher
  • Release : 1989
  • Pages : 619
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
GET BOOK

Current Index to Statistics, Applications, Methods and Theory

Current Index to Statistics, Applications, Methods and Theory
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 1998
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
GET BOOK

The Current Index to Statistics (CIS) is a bibliographic index of publications in statistics, probability, and related fields.