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Author : Gregor Pirš (računalništvo, matematika.)
Publisher :
ISBN 13 :
Total Pages : 149 pages
Book Rating : 4.:/5 (128 download)
Download or read book Bayesian Models for Multivariate Count Data written by Gregor Pirš (računalništvo, matematika.) and published by . This book was released on 2021 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Peter Congdon
Publisher : John Wiley & Sons
ISBN 13 : 0470092386
Total Pages : 446 pages
Book Rating : 4.4/5 (7 download)
Download or read book Bayesian Models for Categorical Data written by Peter Congdon and published by John Wiley & Sons. This book was released on 2005-12-13 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of Bayesian methods for the analysis of data has grown substantially in areas as diverse as applied statistics, psychology, economics and medical science. Bayesian Methods for Categorical Data sets out to demystify modern Bayesian methods, making them accessible to students and researchers alike. Emphasizing the use of statistical computing and applied data analysis, this book provides a comprehensive introduction to Bayesian methods of categorical outcomes. * Reviews recent Bayesian methodology for categorical outcomes (binary, count and multinomial data). * Considers missing data models techniques and non-standard models (ZIP and negative binomial). * Evaluates time series and spatio-temporal models for discrete data. * Features discussion of univariate and multivariate techniques. * Provides a set of downloadable worked examples with documented WinBUGS code, available from an ftp site. The author's previous 2 bestselling titles provided a comprehensive introduction to the theory and application of Bayesian models. Bayesian Models for Categorical Data continues to build upon this foundation by developing their application to categorical, or discrete data - one of the most common types of data available. The author's clear and logical approach makes the book accessible to a wide range of students and practitioners, including those dealing with categorical data in medicine, sociology, psychology and epidemiology.
Author : Siddhartha Chib
Publisher :
ISBN 13 :
Total Pages : 28 pages
Book Rating : 4.:/5 (318 download)
Download or read book Bayesian Analysis of Multivariate Count Data written by Siddhartha Chib and published by . This book was released on 1998 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : A. Colin Cameron
Publisher : Cambridge University Press
ISBN 13 : 1107717795
Total Pages : 597 pages
Book Rating : 4.1/5 (77 download)
Download or read book Regression Analysis of Count Data written by A. Colin Cameron and published by Cambridge University Press. This book was released on 2013-05-27 with total page 597 pages. Available in PDF, EPUB and Kindle. Book excerpt: Students in both social and natural sciences often seek regression methods to explain the frequency of events, such as visits to a doctor, auto accidents, or new patents awarded. This book, now in its second edition, provides the most comprehensive and up-to-date account of models and methods to interpret such data. The authors combine theory and practice to make sophisticated methods of analysis accessible to researchers and practitioners working with widely different types of data and software in areas such as applied statistics, econometrics, marketing, operations research, actuarial studies, demography, biostatistics and quantitative social sciences. The new material includes new theoretical topics, an updated and expanded treatment of cross-section models, coverage of bootstrap-based and simulation-based inference, expanded treatment of time series, multivariate and panel data, expanded treatment of endogenous regressors, coverage of quantile count regression, and a new chapter on Bayesian methods.
Author : Mevin B. Hooten
Publisher : CRC Press
ISBN 13 : 0429513372
Total Pages : 591 pages
Book Rating : 4.4/5 (295 download)
Download or read book Bringing Bayesian Models to Life written by Mevin B. Hooten and published by CRC Press. This book was released on 2019-05-15 with total page 591 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bringing Bayesian Models to Life empowers the reader to extend, enhance, and implement statistical models for ecological and environmental data analysis. We open the black box and show the reader how to connect modern statistical models to computer algorithms. These algorithms allow the user to fit models that answer their scientific questions without needing to rely on automated Bayesian software. We show how to handcraft statistical models that are useful in ecological and environmental science including: linear and generalized linear models, spatial and time series models, occupancy and capture-recapture models, animal movement models, spatio-temporal models, and integrated population-models. Features: R code implementing algorithms to fit Bayesian models using real and simulated data examples. A comprehensive review of statistical models commonly used in ecological and environmental science. Overview of Bayesian computational methods such as importance sampling, MCMC, and HMC. Derivations of the necessary components to construct statistical algorithms from scratch. Bringing Bayesian Models to Life contains a comprehensive treatment of models and associated algorithms for fitting the models to data. We provide detailed and annotated R code in each chapter and apply it to fit each model we present to either real or simulated data for instructional purposes. Our code shows how to create every result and figure in the book so that readers can use and modify it for their own analyses. We provide all code and data in an organized set of directories available at the authors' websites.
Author : Jim Albert
Publisher : CRC Press
ISBN 13 : 1351030132
Total Pages : 553 pages
Book Rating : 4.3/5 (51 download)
Download or read book Probability and Bayesian Modeling written by Jim Albert and published by CRC Press. This book was released on 2019-12-06 with total page 553 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probability and Bayesian Modeling is an introduction to probability and Bayesian thinking for undergraduate students with a calculus background. The first part of the book provides a broad view of probability including foundations, conditional probability, discrete and continuous distributions, and joint distributions. Statistical inference is presented completely from a Bayesian perspective. The text introduces inference and prediction for a single proportion and a single mean from Normal sampling. After fundamentals of Markov Chain Monte Carlo algorithms are introduced, Bayesian inference is described for hierarchical and regression models including logistic regression. The book presents several case studies motivated by some historical Bayesian studies and the authors’ research. This text reflects modern Bayesian statistical practice. Simulation is introduced in all the probability chapters and extensively used in the Bayesian material to simulate from the posterior and predictive distributions. One chapter describes the basic tenets of Metropolis and Gibbs sampling algorithms; however several chapters introduce the fundamentals of Bayesian inference for conjugate priors to deepen understanding. Strategies for constructing prior distributions are described in situations when one has substantial prior information and for cases where one has weak prior knowledge. One chapter introduces hierarchical Bayesian modeling as a practical way of combining data from different groups. There is an extensive discussion of Bayesian regression models including the construction of informative priors, inference about functions of the parameters of interest, prediction, and model selection. The text uses JAGS (Just Another Gibbs Sampler) as a general-purpose computational method for simulating from posterior distributions for a variety of Bayesian models. An R package ProbBayes is available containing all of the book datasets and special functions for illustrating concepts from the book. A complete solutions manual is available for instructors who adopt the book in the Additional Resources section.
Author : Adrian Colin Cameron
Publisher : Cambridge University Press
ISBN 13 : 1107014166
Total Pages : 597 pages
Book Rating : 4.1/5 (7 download)
Download or read book Regression Analysis of Count Data written by Adrian Colin Cameron and published by Cambridge University Press. This book was released on 2013-05-27 with total page 597 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides the most comprehensive and up-to-date account of regression methods to explain the frequency of events.
Author : Santiago Barreda
Publisher : Taylor & Francis
ISBN 13 : 1000869784
Total Pages : 485 pages
Book Rating : 4.0/5 (8 download)
Download or read book Bayesian Multilevel Models for Repeated Measures Data written by Santiago Barreda and published by Taylor & Francis. This book was released on 2023-05-18 with total page 485 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive book is an introduction to multilevel Bayesian models in R using brms and the Stan programming language. Featuring a series of fully worked analyses of repeated measures data, the focus is placed on active learning through the analyses of the progressively more complicated models presented throughout the book. In this book, the authors offer an introduction to statistics entirely focused on repeated measures data beginning with very simple two-group comparisons and ending with multinomial regression models with many ‘random effects’. Across 13 well-structured chapters, readers are provided with all the code necessary to run all the analyses and make all the plots in the book, as well as useful examples of how to interpret and write up their own analyses. This book provides an accessible introduction for readers in any field, with any level of statistical background. Senior undergraduate students, graduate students, and experienced researchers looking to ‘translate’ their skills with more traditional models to a Bayesian framework will benefit greatly from the lessons in this text.
Author : Rainer Winkelmann
Publisher : Springer Science & Business Media
ISBN 13 : 3540247289
Total Pages : 316 pages
Book Rating : 4.5/5 (42 download)
Download or read book Econometric Analysis of Count Data written by Rainer Winkelmann and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graduate students and researchers are provided with an up-to-date survey of statistical and econometric techniques for the analysis of count data, with a focus on conditional distribution models. Proper count data probability models allow for rich inferences, both with respect to the stochastic count process that generated the data, and with respect to predicting the distribution of outcomes. The book starts with a presentation of the benchmark Poisson regression model. Alternative models address unobserved heterogeneity, state dependence, selectivity, endogeneity, underreporting, and clustered sampling. Testing and estimation is discussed from frequentist and Bayesian perspectives. Finally, applications are reviewed in fields such as economics, marketing, sociology, demography, and health sciences. The fourth edition contains several new sections, for example on nonnested hurdle models, quantile regression and on software. Many other sections have been entirely rewritten and extended.
Author : Andrew Gelman
Publisher : CRC Press
ISBN 13 : 1439840954
Total Pages : 677 pages
Book Rating : 4.4/5 (398 download)
Download or read book Bayesian Data Analysis, Third Edition written by Andrew Gelman and published by CRC Press. This book was released on 2013-11-01 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
Author :
Publisher :
ISBN 13 :
Total Pages : 276 pages
Book Rating : 4.:/5 (925 download)
Download or read book Dynamic Modeling of Multivariate Counts written by and published by . This book was released on 2015 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Osval Antonio Montesinos López
Publisher : Springer Nature
ISBN 13 : 3030890104
Total Pages : 707 pages
Book Rating : 4.0/5 (38 download)
Download or read book Multivariate Statistical Machine Learning Methods for Genomic Prediction written by Osval Antonio Montesinos López and published by Springer Nature. This book was released on 2022-02-14 with total page 707 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.
Author : Benjamin Rogers
Publisher :
ISBN 13 :
Total Pages : 121 pages
Book Rating : 4.:/5 (13 download)
Download or read book Bayesian Covariance Modeling for Longitudinal Zero-Inflated Count Data written by Benjamin Rogers and published by . This book was released on 2022 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: We develop models for longitudinal count data with a large number of zeros, a feature known as zero-inflation. Familiar distributions for modeling count data (Poisson, binomial, negative binomial) often do not account for the observed frequency of zeros. Further, in longitudinal data, the same subjects are repeatedly measured over time inducing correlation between sets of measurements on the same individual. Modeling of longitudinal data that does not account for this correlation can give rise to misleading inferences. This dissertation develops three classes of models for longitudinal count data: (i) a Bayesian longitudinal hurdle model for data with prespecified measurement times, (ii) a Bayesian longitudinal hurdle model for data with varying measurement times, and (iii) a multivariate longitudinal zero-inflated Poisson model. Approach (i) is an analysis of the number of days of heaving drinking in a study of screening, brief intervention, and referral to treatment (SBIRT), and approaches (ii) and (iii) are motivated by analyses of the Linking Inmates to Care (LINK LA) study. Building on two-part models that predict non-zero versus zero outcomes while incorporating assumptions about the distribution of non-zero outcomes, the newly developed methods use mixed-effect modeling strategies to account for irregular measurement times and correlated patterns in count data beyond those reflected in random intercept models. The superiority of the proposed methods over random intercept models is established using goodness-of-fit metrics that consider the number of model parameters, and the appeal of modeling multiple count outcomes simultaneously is reflected in Bayesian credible intervals that point to non-zero correlations among the respective count outcomes. We build upon previous longitudinal zero-inflated and hurdle models by introducing time varying random effects in the count models with random effects distributed a priori as multivariate normal with a parameterized covariance matrix. We propose several covariance models, which improve fit over random intercept models in both the SBIRT and LINK LA data. We introduce latent time varying main and random effects to allow count rates and zero probabilities to change with time since intervention and include exposure offsets to account for varying times over which counts are recorded. Finally, for use with multivariate data, we propose a multivariate longitudinal zero-inflated Poisson model for observations with varying exposure, which we use to simultaneously model three different kinds of doctor visits recorded in the LINK LA study.
Author : Peter D. Congdon
Publisher : CRC Press
ISBN 13 : 0429532903
Total Pages : 506 pages
Book Rating : 4.4/5 (295 download)
Download or read book Bayesian Hierarchical Models written by Peter D. Congdon and published by CRC Press. This book was released on 2019-09-16 with total page 506 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Author : Samuel Kotz
Publisher : John Wiley & Sons
ISBN 13 : 0471654035
Total Pages : 752 pages
Book Rating : 4.4/5 (716 download)
Download or read book Continuous Multivariate Distributions, Volume 1 written by Samuel Kotz and published by John Wiley & Sons. This book was released on 2004-04-05 with total page 752 pages. Available in PDF, EPUB and Kindle. Book excerpt: Continuous Multivariate Distributions, Volume 1, Second Edition provides a remarkably comprehensive, self-contained resource for this critical statistical area. It covers all significant advances that have occurred in the field over the past quarter century in the theory, methodology, inferential procedures, computational and simulational aspects, and applications of continuous multivariate distributions. In-depth coverage includes MV systems of distributions, MV normal, MV exponential, MV extreme value, MV beta, MV gamma, MV logistic, MV Liouville, and MV Pareto distributions, as well as MV natural exponential families, which have grown immensely since the 1970s. Each distribution is presented in its own chapter along with descriptions of real-world applications gleaned from the current literature on continuous multivariate distributions and their applications.
Author : Scott M. Lynch
Publisher : Springer Science & Business Media
ISBN 13 : 0387712658
Total Pages : 376 pages
Book Rating : 4.3/5 (877 download)
Download or read book Introduction to Applied Bayesian Statistics and Estimation for Social Scientists written by Scott M. Lynch and published by Springer Science & Business Media. This book was released on 2007-06-30 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book outlines Bayesian statistical analysis in great detail, from the development of a model through the process of making statistical inference. The key feature of this book is that it covers models that are most commonly used in social science research - including the linear regression model, generalized linear models, hierarchical models, and multivariate regression models - and it thoroughly develops each real-data example in painstaking detail.
Author : Jonathan Gruhl
Publisher :
ISBN 13 :
Total Pages : 194 pages
Book Rating : 4.:/5 (826 download)
Download or read book Bayesian Modeling for Multivariate Mixed Outcomes with Applications to Cognitive Testing Data written by Jonathan Gruhl and published by . This book was released on 2012 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation studies parametric and semiparametric approaches to latent variable models, multivariate regression and model-based clustering for mixed outcomes. We use the term mixed outcomes to refer to binary, ordered categorical, count, continuous and other ordered outcomes in combination. Such data structures are common in social, behavioral, and medical sciences. We first review existing parametric approaches to mixed outcomes in latent variable models before developing extensions to accommodate outcome types specific to cognitive testing data. We subsequently develop two new regression approaches for mixed outcome data, the semiparametric Bayesian latent variable model and the semiparametric reduced rank multivariate regression model. In contrast to the existing parametric approaches, these models allow us to avoid specification of distributions for each outcome type. We apply the latent variable and multivariate regression models to investigate the association between cognitive outcomes and MRI-measured regional brain volumes using data from a study of dementia and compare results from the different models. Finally, we develop a new semiparametric correlated partial membership model for model-based clustering of mixed outcome data that also allows us to avoid specification of outcome distributions. We demonstrate our semiparametric approach to model-based clustering on NBA player data from the 2010-2011 season as well as on cognitive testing data from a study of dementia.
