Flexible Bayesian Regression Modelling

Author : Yanan Fan
Publisher : Academic Press
ISBN 13 : 0128158638
Total Pages : 302 pages
Book Rating : 4.30/5 ( download)

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Download or read book Flexible Bayesian Regression Modelling written by Yanan Fan and published by Academic Press. This book was released on 2019-10-30 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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’

Author : David G. T. Denison
Publisher : John Wiley & Sons
ISBN 13 : 9780471490364
Total Pages : 302 pages
Book Rating : 4.69/5 ( download)

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Download or read book Bayesian Methods for Nonlinear Classification and Regression written by David G. T. Denison and published by John Wiley & Sons. This book was released on 2002-05-06 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bei der Regressionsanalyse von Datenmaterial erhält man leider selten lineare oder andere einfache Zusammenhänge (parametrische Modelle). Dieses Buch hilft Ihnen, auch komplexere, nichtparametrische Modelle zu verstehen und zu beherrschen. Stärken und Schwächen jedes einzelnen Modells werden durch die Anwendung auf Standarddatensätze demonstriert. Verbreitete nichtparametrische Modelle werden mit Hilfe von Bayes-Verfahren in einen kohärenten wahrscheinlichkeitstheoretischen Zusammenhang gebracht.

Author : Christos H. Skiadas
Publisher : John Wiley & Sons
ISBN 13 : 1786303825
Total Pages : 286 pages
Book Rating : 4.20/5 ( download)

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Download or read book Data Analysis and Applications 1 written by Christos H. Skiadas and published by John Wiley & Sons. This book was released on 2019-05-21 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 equations, and SME modeling; and Part 3 presents symbolic data analysis, time series and multiple choice models, modeling in demography, and data mining.

Author : Xiaofeng Wang
Publisher : CRC Press
ISBN 13 : 1351165747
Total Pages : 325 pages
Book Rating : 4.47/5 ( download)

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Download or read book Bayesian Regression Modeling with INLA written by Xiaofeng Wang and published by CRC Press. This book was released on 2018-01-29 with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt: INLA stands for Integrated Nested Laplace Approximations, which is a new method for fitting a broad class of Bayesian regression models. No samples of the posterior marginal distributions need to be drawn using INLA, so it is a computationally convenient alternative to Markov chain Monte Carlo (MCMC), the standard tool for Bayesian inference. Bayesian Regression Modeling with INLA covers a wide range of modern regression models and focuses on the INLA technique for building Bayesian models using real-world data and assessing their validity. A key theme throughout the book is that it makes sense to demonstrate the interplay of theory and practice with reproducible studies. Complete R commands are provided for each example, and a supporting website holds all of the data described in the book. An R package including the data and additional functions in the book is available to download. The book is aimed at readers who have a basic knowledge of statistical theory and Bayesian methodology. It gets readers up to date on the latest in Bayesian inference using INLA and prepares them for sophisticated, real-world work. Xiaofeng Wang is Professor of Medicine and Biostatistics at the Cleveland Clinic Lerner College of Medicine of Case Western Reserve University and a Full Staff in the Department of Quantitative Health Sciences at Cleveland Clinic. Yu Ryan Yue is Associate Professor of Statistics in the Paul H. Chook Department of Information Systems and Statistics at Baruch College, The City University of New York. Julian J. Faraway is Professor of Statistics in the Department of Mathematical Sciences at the University of Bath.

Author : Ivan Jeliazkov
Publisher : Emerald Group Publishing
ISBN 13 : 1838674217
Total Pages : 252 pages
Book Rating : 4.12/5 ( download)

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Download or read book Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling written by Ivan Jeliazkov and published by Emerald Group Publishing. This book was released on 2019-10-18 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Author : Vanda Inácio de Carvalho
Publisher : Chapman and Hall/CRC
ISBN 13 : 9781466580398
Total Pages : 250 pages
Book Rating : 4.99/5 ( download)

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Download or read book Flexible Bayesian Models for Medical Diagnostic Data written by Vanda Inácio de Carvalho and published by Chapman and Hall/CRC. This book was released on 2016-05-15 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Author : Yiannis Dimotikalis
Publisher : Wiley-ISTE
ISBN 13 : 9781786306746
Total Pages : 0 pages
Book Rating : 4.43/5 ( download)

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Download or read book Applied Modeling Techniques and Data Analysis 2 written by Yiannis Dimotikalis and published by Wiley-ISTE. This book was released on 2021-05-11 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 this topic, meeting increasing demand from wide circles of the scientific community. Applied Modeling Techniques and Data Analysis 2 is a collective work by a number of leading scientists, analysts, engineers, mathematicians and statisticians, working on the front end of data analysis and modeling applications. The chapters cover a cross section of current concerns and research interests in the above scientific areas. The collected material is divided into appropriate sections to provide the reader with both theoretical and applied information on data analysis methods, models and techniques, along with appropriate applications.

Author : Michael D. Lee
Publisher : Cambridge University Press
ISBN 13 : 1107653916
Total Pages : 279 pages
Book Rating : 4.17/5 ( download)

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Download or read book Bayesian Cognitive Modeling written by Michael D. Lee and published by Cambridge University Press. This book was released on 2014-04-03 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian inference has become a standard method of analysis in many fields of science. Students and researchers in experimental psychology and cognitive science, however, have failed to take full advantage of the new and exciting possibilities that the Bayesian approach affords. Ideal for teaching and self study, this book demonstrates how to do Bayesian modeling. Short, to-the-point chapters offer examples, exercises, and computer code (using WinBUGS or JAGS, and supported by Matlab and R), with additional support available online. No advance knowledge of statistics is required and, from the very start, readers are encouraged to apply and adjust Bayesian analyses by themselves. The book contains a series of chapters on parameter estimation and model selection, followed by detailed case studies from cognitive science. After working through this book, readers should be able to build their own Bayesian models, apply the models to their own data, and draw their own conclusions.

Author : Osvaldo Martin
Publisher : Packt Publishing Ltd
ISBN 13 : 1785889850
Total Pages : 282 pages
Book Rating : 4.51/5 ( download)

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Download or read book Bayesian Analysis with Python written by Osvaldo Martin and published by Packt Publishing Ltd. This book was released on 2016-11-25 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unleash the power and flexibility of the Bayesian framework About This Book Simplify the Bayes process for solving complex statistical problems using Python; Tutorial guide that will take the you through the journey of Bayesian analysis with the help of sample problems and practice exercises; Learn how and when to use Bayesian analysis in your applications with this guide. Who This Book Is For Students, researchers and data scientists who wish to learn Bayesian data analysis with Python and implement probabilistic models in their day to day projects. Programming experience with Python is essential. No previous statistical knowledge is assumed. What You Will Learn Understand the essentials Bayesian concepts from a practical point of view Learn how to build probabilistic models using the Python library PyMC3 Acquire the skills to sanity-check your models and modify them if necessary Add structure to your models and get the advantages of hierarchical models Find out how different models can be used to answer different data analysis questions When in doubt, learn to choose between alternative models. Predict continuous target outcomes using regression analysis or assign classes using logistic and softmax regression. Learn how to think probabilistically and unleash the power and flexibility of the Bayesian framework In Detail The purpose of this book is to teach the main concepts of Bayesian data analysis. We will learn how to effectively use PyMC3, a Python library for probabilistic programming, to perform Bayesian parameter estimation, to check models and validate them. This book begins presenting the key concepts of the Bayesian framework and the main advantages of this approach from a practical point of view. Moving on, we will explore the power and flexibility of generalized linear models and how to adapt them to a wide array of problems, including regression and classification. We will also look into mixture models and clustering data, and we will finish with advanced topics like non-parametrics models and Gaussian processes. With the help of Python and PyMC3 you will learn to implement, check and expand Bayesian models to solve data analysis problems. Style and approach Bayes algorithms are widely used in statistics, machine learning, artificial intelligence, and data mining. This will be a practical guide allowing the readers to use Bayesian methods for statistical modelling and analysis using Python.

Author : Ronald Christensen
Publisher : CRC Press
ISBN 13 : 1439803552
Total Pages : 518 pages
Book Rating : 4.54/5 ( download)

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Download or read book Bayesian Ideas and Data Analysis written by Ronald Christensen and published by CRC Press. This book was released on 2011-07-07 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.