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Author: J.L. Schafer Publisher: CRC Press ISBN: 9781439821862 Category : Mathematics Languages : en Pages : 470
Book Description
The last two decades have seen enormous developments in statistical methods for incomplete data. The EM algorithm and its extensions, multiple imputation, and Markov Chain Monte Carlo provide a set of flexible and reliable tools from inference in large classes of missing-data problems. Yet, in practical terms, those developments have had surprisingly little impact on the way most data analysts handle missing values on a routine basis. Analysis of Incomplete Multivariate Data helps bridge the gap between theory and practice, making these missing-data tools accessible to a broad audience. It presents a unified, Bayesian approach to the analysis of incomplete multivariate data, covering datasets in which the variables are continuous, categorical, or both. The focus is applied, where necessary, to help readers thoroughly understand the statistical properties of those methods, and the behavior of the accompanying algorithms. All techniques are illustrated with real data examples, with extended discussion and practical advice. All of the algorithms described in this book have been implemented by the author for general use in the statistical languages S and S Plus. The software is available free of charge on the Internet.
Author: James E. Gentle Publisher: Springer Science & Business Media ISBN: 0387216111 Category : Computers Languages : en Pages : 427
Book Description
Will provide a more elementary introduction to these topics than other books available; Gentle is the author of two other Springer books
Author: Norman L. Johnson Publisher: John Wiley & Sons ISBN: 0471584940 Category : Mathematics Languages : en Pages : 747
Book Description
Comprehensive reference for statistical distributions Continuous Univariate Distributions, Volume 2 provides in-depth reference for anyone who applies statistical distributions in fields including engineering, business, economics, and the sciences. Covering a range of distributions, both common and uncommon, this book includes guidance toward extreme value, logistics, Laplace, beta, rectangular, noncentral distributions and more. Each distribution is presented individually for ease of reference, with clear explanations of methods of inference, tolerance limits, applications, characterizations, and other important aspects, including reference to other related distributions.
Author: Jeff Gill Publisher: CRC Press ISBN: 1584885629 Category : Mathematics Languages : en Pages : 696
Book Description
The first edition of Bayesian Methods: A Social and Behavioral Sciences Approach helped pave the way for Bayesian approaches to become more prominent in social science methodology. While the focus remains on practical modeling and basic theory as well as on intuitive explanations and derivations without skipping steps, this second edition incorporates the latest methodology and recent changes in software offerings. New to the Second Edition Two chapters on Markov chain Monte Carlo (MCMC) that cover ergodicity, convergence, mixing, simulated annealing, reversible jump MCMC, and coupling Expanded coverage of Bayesian linear and hierarchical models More technical and philosophical details on prior distributions A dedicated R package (BaM) with data and code for the examples as well as a set of functions for practical purposes such as calculating highest posterior density (HPD) intervals Requiring only a basic working knowledge of linear algebra and calculus, this text is one of the few to offer a graduate-level introduction to Bayesian statistics for social scientists. It first introduces Bayesian statistics and inference, before moving on to assess model quality and fit. Subsequent chapters examine hierarchical models within a Bayesian context and explore MCMC techniques and other numerical methods. Concentrating on practical computing issues, the author includes specific details for Bayesian model building and testing and uses the R and BUGS software for examples and exercises.
Author: Jaromir Antoch Publisher: Springer Science & Business Media ISBN: 364299766X Category : Business & Economics Languages : en Pages : 289
Book Description
Although no-one is, probably, too enthused about the idea, it is a fact that the development of most empirical sciences to a great extend depends of the development of data analysis methods and techniques, which, due to the necessity of applications of computers for that pur pose, actually means that it practically depends on the advancements and orientation of computational statistics. This volume contains complete texts of the lectures held during the Summer School on "Computational Aspects of Model Choice" orga nized jointly by Charles University, Prague, and International Associa tion for Statistical Computing (IASC) on July 1-14, 1991, in Prague. Main aims of the Summer School were to review and analyse some of the recent developments concerning computational aspects of the model choice as well as their theoretical background. The topics covers the problems of the change point detection, robust estimation and its computational aspects, classification using binary trees, stochastic ap proximation and optimization including the discussion about available software, computational aspects of graphical model selection and mul tiple hypotheses testing. The bridge between these different approaches is formed by the survey paper about statistical applications of artificial intelligence.
Author: Publisher: Elsevier ISBN: 0080536476 Category : Mathematics Languages : en Pages : 449
Book Description
In this volume contemporary methods designed to provide insights into, mathematical structure for, and predictive inferences about neuroendocrine control mechanisms are presented. - Collates an array of contemporary techniques for analysis of neuroendocrine data - Discusses current problems in and solutions to neurohormone pulse analysis - Identifies relevant software available
Author: Stef van Buuren Publisher: CRC Press ISBN: 0429960344 Category : Mathematics Languages : en Pages : 309
Book Description
Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes, like deletion or mean imputation, only work under highly restrictive conditions, which are often not met in practice. Multiple imputation replaces each missing value by multiple plausible values. The variability between these replacements reflects our ignorance of the true (but missing) value. Each of the completed data set is then analyzed by standard methods, and the results are pooled to obtain unbiased estimates with correct confidence intervals. Multiple imputation is a general approach that also inspires novel solutions to old problems by reformulating the task at hand as a missing-data problem. This is the second edition of a popular book on multiple imputation, focused on explaining the application of methods through detailed worked examples using the MICE package as developed by the author. This new edition incorporates the recent developments in this fast-moving field. This class-tested book avoids mathematical and technical details as much as possible: formulas are accompanied by verbal statements that explain the formula in accessible terms. The book sharpens the reader’s intuition on how to think about missing data, and provides all the tools needed to execute a well-grounded quantitative analysis in the presence of missing data.
Author: American Statistical Association. Statistical Computing Section
Author: American Statistical Association. Statistical Computing Section
Author: 
Author: Madan L. Puri
Author: J.L. Schafer
Author: James E. Gentle
Author: Norman L. Johnson
Author: Jeff Gill
Author: Jaromir Antoch
Author: 
Author: Stef van Buuren