Flexible Bayesian Regression Modelling

Flexible Bayesian Regression Modelling
Author: Yanan Fan,David Nott,Mike S. Smith,Jean-Luc Dortet-Bernadet
Publsiher: Academic Press
Total Pages: 302
Release: 2019-10-30
ISBN 10: 0128158638
ISBN 13: 9780128158630
Language: EN, FR, DE, ES & NL

Flexible Bayesian Regression Modelling Book Review:

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
Author: José M. Bernardo,M. J. Bayarri,James O. Berger,A. P. Dawid,David Heckerman
Publsiher: Oxford University Press
Total Pages: 706
Release: 2011-10-06
ISBN 10: 0199694583
ISBN 13: 9780199694587
Language: EN, FR, DE, ES & NL

Bayesian Statistics 9 Book Review:

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
Author: Anthony O' Hagan,Mike West
Publsiher: OUP Oxford
Total Pages: 924
Release: 2010-03-18
ISBN 10: 0191613894
ISBN 13: 9780191613890
Language: EN, FR, DE, ES & NL

The Oxford Handbook of Applied Bayesian Analysis Book Review:

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.

Bayesian Methods for Nonlinear Classification and Regression

Bayesian Methods for Nonlinear Classification and Regression
Author: David G. T. Denison,Christopher C. Holmes,Bani K. Mallick,Adrian F. M. Smith
Publsiher: John Wiley & Sons
Total Pages: 296
Release: 2002-05-06
ISBN 10: 9780471490364
ISBN 13: 0471490369
Language: EN, FR, DE, ES & NL

Bayesian Methods for Nonlinear Classification and Regression Book Review:

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.

Journal of the American Statistical Association

Journal of the American Statistical Association
Author: Anonim
Publsiher: Unknown
Total Pages: 329
Release: 2008
ISBN 10:
ISBN 13: UCSD:31822036057495
Language: EN, FR, DE, ES & NL

Journal of the American Statistical Association Book Review:

Bayesian Statistics 6

Bayesian Statistics 6
Author: José M. Bernardo,James O. Berger,A. P. Dawid,Adrian F. M. Smith
Publsiher: Oxford University Press
Total Pages: 867
Release: 1999-08-12
ISBN 10: 9780198504856
ISBN 13: 0198504853
Language: EN, FR, DE, ES & NL

Bayesian Statistics 6 Book Review:

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 Hierarchical Models

Bayesian Hierarchical Models
Author: Peter D. Congdon
Publsiher: CRC Press
Total Pages: 580
Release: 2019-09-16
ISBN 10: 1498785913
ISBN 13: 9781498785914
Language: EN, FR, DE, ES & NL

Bayesian Hierarchical Models Book Review:

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 and data analysis, but now using entirely R-based Bayesian computing options. It has been updated with a new chapter on regression for causal effects, and one on computing options and strategies. This latter chapter is particularly important, due to recent advances in Bayesian computing and estimation, including the development of rjags and rstan. It also features updates throughout with new examples. The examples exploit and illustrate the broader advantages of the R computing environment, while allowing readers to explore alternative likelihood assumptions, regression structures, and assumptions on prior densities. Features: Provides a comprehensive and accessible overview of applied Bayesian hierarchical modelling Includes many real data examples to illustrate different modelling topics R code (based on rjags, jagsUI, R2OpenBUGS, and rstan) is integrated into the book, emphasizing implementation Software options and coding principles are introduced in new chapter on computing Programs and data sets available on the book’s website

Bayesian Ideas and Data Analysis

Bayesian Ideas and Data Analysis
Author: Ronald Christensen,Wesley Johnson,Adam Branscum,Timothy E Hanson
Publsiher: CRC Press
Total Pages: 516
Release: 2011-07-07
ISBN 10: 1439803552
ISBN 13: 9781439803554
Language: EN, FR, DE, ES & NL

Bayesian Ideas and Data Analysis Book Review:

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.

Statistical Modelling and Regression Structures

Statistical Modelling and Regression Structures
Author: Thomas Kneib,Gerhard Tutz
Publsiher: Springer Science & Business Media
Total Pages: 472
Release: 2010-01-12
ISBN 10: 3790824135
ISBN 13: 9783790824131
Language: EN, FR, DE, ES & NL

Statistical Modelling and Regression Structures Book Review:

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.

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
Publsiher: Chapman and Hall/CRC
Total Pages: 250
Release: 2016-05-15
ISBN 10: 9781466580398
ISBN 13: 1466580399
Language: EN, FR, DE, ES & NL

Flexible Bayesian Models for Medical Diagnostic Data Book Review:

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
Author: Anonim
Publsiher: Unknown
Total Pages: 329
Release: 2001
ISBN 10:
ISBN 13: UCBK:C078266509
Language: EN, FR, DE, ES & NL

Statistical Theory and Method Abstracts Book Review:

Bayesian Regression Modeling with INLA

Bayesian Regression Modeling with INLA
Author: Xiaofeng Wang,Yu Yue Ryan,Julian J. Faraway
Publsiher: CRC Press
Total Pages: 312
Release: 2018-01-29
ISBN 10: 1351165747
ISBN 13: 9781351165747
Language: EN, FR, DE, ES & NL

Bayesian Regression Modeling with INLA Book Review:

This book addresses the applications of extensively used regression models under a Bayesian framework. It emphasizes efficient Bayesian inference through integrated nested Laplace approximations (INLA) and real data analysis using R. The INLA method directly computes very accurate approximations to the posterior marginal distributions and is a promising alternative to Markov chain Monte Carlo (MCMC) algorithms, which come with a range of issues that impede practical use of Bayesian models.

Introduction to Hierarchical Bayesian Modeling for Ecological Data

Introduction to Hierarchical Bayesian Modeling for Ecological Data
Author: Eric Parent,Etienne Rivot
Publsiher: CRC Press
Total Pages: 427
Release: 2012-08-21
ISBN 10: 1584889195
ISBN 13: 9781584889199
Language: EN, FR, DE, ES & NL

Introduction to Hierarchical Bayesian Modeling for Ecological Data Book Review:

Making statistical modeling and inference more accessible to ecologists and related scientists, Introduction to Hierarchical Bayesian Modeling for Ecological Data gives readers a flexible and effective framework to learn about complex ecological processes from various sources of data. It also helps readers get started on building their own statistical models. The text begins with simple models that progressively become more complex and realistic through explanatory covariates and intermediate hidden states variables. When fitting the models to data, the authors gradually present the concepts and techniques of the Bayesian paradigm from a practical point of view using real case studies. They emphasize how hierarchical Bayesian modeling supports multidimensional models involving complex interactions between parameters and latent variables. Data sets, exercises, and R and WinBUGS codes are available on the authors’ website. This book shows how Bayesian statistical modeling provides an intuitive way to organize data, test ideas, investigate competing hypotheses, and assess degrees of confidence of predictions. It also illustrates how conditional reasoning can dismantle a complex reality into more understandable pieces. As conditional reasoning is intimately linked with Bayesian thinking, considering hierarchical models within the Bayesian setting offers a unified and coherent framework for modeling, estimation, and prediction.

Data Analysis and Applications 1

Data Analysis and Applications 1
Author: Christos H. Skiadas,James R. Bozeman
Publsiher: John Wiley & Sons
Total Pages: 286
Release: 2019-03-04
ISBN 10: 1119597579
ISBN 13: 9781119597575
Language: EN, FR, DE, ES & NL

Data Analysis and Applications 1 Book Review:

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.

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
Publsiher: Academic Press
Total Pages: 328
Release: 2015-04-04
ISBN 10: 0128016787
ISBN 13: 9780128016787
Language: EN, FR, DE, ES & NL

Bayesian Data Analysis in Ecology Using Linear Models with R BUGS and Stan Book Review:

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 Bayesian software, using R for the simple modes, and flexible Bayesian software (BUGS and Stan) for the more complicated ones. Guiding the ready from easy toward more complex (real) data analyses ina step-by-step manner, the book presents problems and solutions—including all R codes—that are most often applicable to other data and questions, making it an invaluable resource for analyzing a variety of data types. Introduces Bayesian data analysis, allowing users to obtain uncertainty measurements easily for any derived parameter of interest Written in a step-by-step approach that allows for eased understanding by non-statisticians Includes a companion website containing R-code to help users conduct Bayesian data analyses on their own data All example data as well as additional functions are provided in the R-package blmeco

Data Analysis Using Regression and Multilevel Hierarchical Models

Data Analysis Using Regression and Multilevel Hierarchical Models
Author: Andrew Gelman,Professor in the Department of Statistics Andrew Gelman,Jennifer Hill
Publsiher: Cambridge University Press
Total Pages: 625
Release: 2007
ISBN 10: 9780521686891
ISBN 13: 052168689X
Language: EN, FR, DE, ES & NL

Data Analysis Using Regression and Multilevel Hierarchical Models Book Review:

This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.

Regression

Regression
Author: Ludwig Fahrmeir,Thomas Kneib,Stefan Lang,Brian Marx
Publsiher: Springer Science & Business Media
Total Pages: 698
Release: 2013-05-09
ISBN 10: 3642343333
ISBN 13: 9783642343339
Language: EN, FR, DE, ES & NL

Regression Book Review:

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 and practitioners in social, economic, and life sciences, as well as students and teachers in statistics programs, and mathematicians and computer scientists with interests in statistical modeling and data analysis. It is written on an intermediate mathematical level and assumes only knowledge of basic probability, calculus, and statistics. The most important definitions and statements are concisely summarized in boxes. Two appendices describe required matrix algebra, as well as elements of probability calculus and statistical inference.

Statistical Modeling and Computation

Statistical Modeling and Computation
Author: Dirk P. Kroese,Joshua C.C. Chan
Publsiher: Springer Science & Business Media
Total Pages: 400
Release: 2013-11-18
ISBN 10: 1461487757
ISBN 13: 9781461487753
Language: EN, FR, DE, ES & NL

Statistical Modeling and Computation Book Review:

This textbook on statistical modeling and statistical inference will assist advanced undergraduate and graduate students. Statistical Modeling and Computation provides a unique introduction to modern Statistics from both classical and Bayesian perspectives. It also offers an integrated treatment of Mathematical Statistics and modern statistical computation, emphasizing statistical modeling, computational techniques, and applications. Each of the three parts will cover topics essential to university courses. Part I covers the fundamentals of probability theory. In Part II, the authors introduce a wide variety of classical models that include, among others, linear regression and ANOVA models. In Part III, the authors address the statistical analysis and computation of various advanced models, such as generalized linear, state-space and Gaussian models. Particular attention is paid to fast Monte Carlo techniques for Bayesian inference on these models. Throughout the book the authors include a large number of illustrative examples and solved problems. The book also features a section with solutions, an appendix that serves as a MATLAB primer, and a mathematical supplement.​

An R Companion to Applied Regression

An R Companion to Applied Regression
Author: John Fox,Sanford Weisberg
Publsiher: SAGE Publications
Total Pages: 608
Release: 2018-09-27
ISBN 10: 1544336454
ISBN 13: 9781544336459
Language: EN, FR, DE, ES & NL

An R Companion to Applied Regression Book Review:

An R Companion to Applied Regression is a broad introduction to the R statistical computing environment in the context of applied regression analysis. John Fox and Sanford Weisberg provide a step-by-step guide to using the free statistical software R, an emphasis on integrating statistical computing in R with the practice of data analysis, coverage of generalized linear models, and substantial web-based support materials. The Third Edition has been reorganized and includes a new chapter on mixed-effects models, new and updated data sets, and a de-emphasis on statistical programming, while retaining a general introduction to basic R programming. The authors have substantially updated both the car and effects packages for R for this edition, introducing additional capabilities and making the software more consistent and easier to use. They also advocate an everyday data-analysis workflow that encourages reproducible research. To this end, they provide coverage of RStudio, an interactive development environment for R that allows readers to organize and document their work in a simple and intuitive fashion, and then easily share their results with others. Also included is coverage of R Markdown, showing how to create documents that mix R commands with explanatory text.

Application of Gaussian Process Priors on Bayesian Regression

Application of Gaussian Process Priors on Bayesian Regression
Author: Abhishek Bishoyi
Publsiher: Unknown
Total Pages: 329
Release: 2017
ISBN 10:
ISBN 13: OCLC:1196371746
Language: EN, FR, DE, ES & NL

Application of Gaussian Process Priors on Bayesian Regression Book Review:

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.