Bayesian Analysis in Pharmaceutical Development and Clinical Research, Shayene C. Gad
Автор: Gelman Название: Bayesian Data Analysis, Third Edition ISBN: 1439840954 ISBN-13(EAN): 9781439840955 Издательство: Taylor&Francis Рейтинг: Цена: 12318 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis 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.
Описание: The cross-disciplinary pursuits between modern technology, their computations and applications to the human body have exploded because of rapid developments in computer technology and mathematical computational techniques. This four-volume set, Computational Methods in Biophysics, Biomaterials, Biotechnology and Medical Systems, represents the first multi-volume treatment of this significant subject on the international scene. The work is an indispensable reference source by leading researchers, and is essential reference work for academics, practitioners, students and researchers working with: *Computers in Medicine, *Science and Mathematics in Biomaterials, Biomechanics and Bioengineering, *Computational Biophysics.
Описание: The cross-disciplinary pursuits between modern technology, their computations and applications to the human body have exploded because of rapid developments in computer technology and mathematical computational techniques. This four-volume set, Computational Methods in Biophysics, Biomaterials, Biotechnology and Medical Systems, represents the first multi-volume treatment of this significant subject on the international scene. The work is an indispensable reference source by leading researchers, and is essential reference work for academics, practitioners, students and researchers working with: *Computers in Medicine, *Science and Mathematics in Biomaterials, Biomechanics and Bioengineering, *Computational Biophysics. Volume Synopsis: Volume 1: Algorithm Techniques The first volume begins logically with algorithmic techniques and their application in medical imaging procedures Electrocardiography (ECG) and Magnetoencephalography (MEG): modeling techniques used in data rich biomechanics and biomedical environments; and specific coverage of artificial neural networks for rehabilitation and DNA sequence analysis. Volume 2: Computational Methods Volume 2 presents computational methods across a broad array of subjects, from bone implants and wound healing to temperature driven physiological reactions of arms and legs; from spatial pattern analysis and binocular fixation systems to telerobotic surgery; and computer design and manufacturing systems in biomedicine. Volume 3: Mathematical Analysis Methods This volume focuses on methods of mathematical analysis, starting with medical imaging. Then to biological modeling for applications to blood flow, neuranatomy and motion analysis of tumors and metastases; mathematical methods related to Doppler examination of the heart, blood pressure drugs and knee implants; and closing with biosignal interchange formats and boundary element methods for biological systems. Volume 4: Diagnostic Methods This volume presents a rich variety of diagnostic methods and techniques derived from results of the earlier volumes. From analytical to heuristic to modern methods of artificial intelligence, a significant summary is provided on the essential role computational methods play in medical systems.
Описание: An Introduction to Survival Analysis Using Stata, Revised Third Edition is the ideal tutorial for professional data analysts who want to learn survival analysis for the first time or who are well versed in survival analysis but are not as dexterous in using Stata to analyze survival data. This text also serves as a valuable reference to those readers who already have experience using Stata’s survival analysis routines. The revised third edition has been updated for Stata 14, and it includes a new section on predictive margins and marginal effects, which demonstrates how to obtain and visualize marginal predictions and marginal effects using the margins and marginsplot commands after survival regression models. Survival analysis is a field of its own that requires specialized data management and analysis procedures. To meet this requirement, Stata provides the st family of commands for organizing and summarizing survival data. This book provides statistical theory, step-by-step procedures for analyzing survival data, an in-depth usage guide for Stata's most widely used st commands, and a collection of tips for using Stata to analyze survival data and to present the results. This book develops from first principles the statistical concepts unique to survival data and assumes only a knowledge of basic probability and statistics and a working knowledge of Stata. The first three chapters of the text cover basic theoretical concepts: hazard functions, cumulative hazard functions, and their interpretations; survivor functions; hazard models; and a comparison of nonparametric, semiparametric, and parametric methodologies. Chapter 4 deals with censoring and truncation. The next three chapters cover the formatting, manipulation, stsetting, and error checking involved in preparing survival data for analysis using Stata's st analysis commands. Chapter 8 covers nonparametric methods, including the Kaplan–Meier and Nelson–Aalen estimators and the various nonparametric tests for the equality of survival experience. Chapters 9–11 discuss Cox regression and include various examples of fitting a Cox model, obtaining predictions, interpreting results, building models, model diagnostics, and regression with survey data. The next four chapters cover parametric models, which are fit using Stata's streg command. These chapters include detailed derivations of all six parametric models currently supported in Stata and methods for determining which model is appropriate, as well as information on stratification, obtaining predictions, and advanced topics such as frailty models. Chapter 16 is devoted to power and sample-size calculations for survival studies. The final chapter covers survival analysis in the presence of competing risks.
Описание: Going beyond performing simple analyses, researchers involved in the highly dynamic field of computational intelligent data analysis design algorithms that solve increasingly complex data problems in changing environments, including economic, environmental, and social data. Computational Intelligent Data Analysis for Sustainable Development presents novel methodologies for automatically processing these types of data to support rational decision making for sustainable development. Through numerous case studies and applications, it illustrates important data analysis methods, including mathematical optimization, machine learning, signal processing, and temporal and spatial analysis, for quantifying and describing sustainable development problems. With a focus on integrated sustainability analysis, the book presents a large-scale quadratic programming algorithm to expand high-resolution input-output tables from the national scale to the multinational scale to measure the carbon footprint of the entire trade supply chain. It also quantifies the error or dispersion between different reclassification and aggregation schemas, revealing that aggregation errors have a high concentration over specific regions and sectors. The book summarizes the latest contributions of the data analysis community to climate change research. A profuse amount of climate data of various types is available, providing a rich and fertile playground for future data mining and machine learning research. The book also pays special attention to several critical challenges in the science of climate extremes that are not handled by the current generation of climate models. It discusses potential conceptual and methodological directions to build a close integration between physical understanding, or physics-based modeling, and data-driven insights. The book then covers the conservation of species and ecologically valuable land. A case study on the Pennsylvania Dirt and Gravel Roads Program demonstrates that multiple-objective linear programming is a more versatile and efficient approach than the widely used benefit targeting selection process. Moving on to renewable energy and the need for smart grids, the book explores how the ongoing transformation to a sustainable energy system of renewable sources leads to a paradigm shift from demand-driven generation to generation-driven demand. It shows how to maximize renewable energy as electricity by building a supergrid or mixing renewable sources with demand management and storage. It also presents intelligent data analysis for real-time detection of disruptive events from power system frequency data collected using an existing Internet-based frequency monitoring network as well as evaluates a set of computationally intelligent techniques for long-term wind resource assessment. In addition, the book gives an example of how temporal and spatial data analysis tools are used to gather knowledge about behavioral data and address important social problems such as criminal offenses. It also applies constraint logic programming to a planning problem: the environmental and social impact assessment of the regional energy plan of the Emilia-Romagna region of Italy. Sustainable development problems, such as global warming, resource shortages, global species loss, and pollution, push researchers to create powerful data analysis approaches that analysts can then use to gain insight into these issues to support rational decision making. This volume shows both the data analysis and sustainable development communities how to use intelligent data analysis tools to address practical problems and encourages researchers to develop better methods.
Описание: This proven text provides an accessible introduction to the foundations and applications of Bayesian analysis. Broadening its scope to nonstatisticians, this edition concentrates more on hierarchical Bayesian modeling as implemented via MCMC methods and related data analytic techniques. It contains a reader-friendly introduction to hierarchical statistical modeling, a new chapter on Bayesian design that emphasizes Bayesian clinical trials, a completely revised and expanded section on ranking and histogram estimation, and a new case study on infectious disease modeling and the 1918 flu epidemic. This edition includes new data examples, corresponding R and WinBUGS code, and exercises.
Описание: This comprehensive work explores the broad question of how the English courts dealt with crime in the period during which the foundations of modern forms of judicial administration were being laid. Includes basic Bayesian ideas, prior distributions, clinical trials, observational studies, evidence synthesis and cost effectiveness analysis. Illustrated throughout by detailed case studies and worked examples, this book includes exercises in all the chapters.
Автор: Ghosh Название: An Introduction to Bayesian Analysis ISBN: 0387400842 ISBN-13(EAN): 9780387400846 Издательство: Springer Рейтинг: Цена: 10394 р. Наличие на складе: Поставка под заказ.
Описание: A textbook on Bayesian analysis blending modern Bayesian theory, methods, and applications. It covers advances in both low-dimensional and high-dimensional problems, as well as important topics such as empirical Bayes and hierarchical Bayes methods and Markov chain Monte Carlo (MCMC) techniques.
Описание: Facing rapidly growing challenges in empirical research, this volume presents a selection of new methods and approaches in the field of Exploratory Data Analysis. The interested reader will find numerous ideas and examples for cross disciplinary applications of classification and data analysis methods in fields such as data and web mining, medicine and biological sciences as well as marketing, finance and management sciences.
Автор: Ibrahim Joseph G., Chen Ming-Hui, Sinha Debajyoti Название: Bayesian Survival Analysis ISBN: 0387952772 ISBN-13(EAN): 9780387952772 Издательство: Springer Рейтинг: Цена: 17324 р. Наличие на складе: Поставка под заказ.
Описание: Offers a treatment of Bayesian survival analysis. This book addresses various topics, including parametric models, semiparametric models based on prior processes, proportional and non-proportional hazards models, frailty models, cure rate models, model selection and comparison, and joint models for longitudinal and survival data.
Автор: Myers, Jerome L Название: Research design and statistical analysis ISBN: 0805864318 ISBN-13(EAN): 9780805864311 Издательство: Taylor&Francis Рейтинг: Цена: 16500 р. Наличие на складе: Невозможна поставка.
Описание: Provides coverage of the design principles and statistical concepts necessary to make sense of real data. This book provides an integrated example of how to apply the concepts and procedures covered in the chapters of the section. It reviews research planning and data exploration in statistics. It is suitable for practicing researchers.
Автор: Thompson John Название: Bayesian Analysis with Stata ISBN: 1597181412 ISBN-13(EAN): 9781597181419 Издательство: Taylor&Francis Рейтинг: Цена: 7699 р. Наличие на складе: Невозможна поставка.
Bayesian Analysis with Stata is written for anyone interested in applying Bayesian methods to real data easily. The book shows how modern analyses based on Markov chain Monte Carlo (MCMC) methods are implemented in Stata both directly and by passing Stata datasets to OpenBUGS or WinBUGS for computation, allowing Stata's data management and graphing capability to be used with OpenBUGS/WinBUGS speed and reliability.
The book emphasizes practical data analysis from the Bayesian perspective, and hence covers the selection of realistic priors, computational efficiency and speed, the assessment of convergence, the evaluation of models, and the presentation of the results. Every topic is illustrated in detail using real-life examples, mostly drawn from medical research.
The book takes great care in introducing concepts and coding tools incrementally so that there are no steep patches or discontinuities in the learning curve. The book's content helps the user see exactly what computations are done for simple standard models and shows the user how those computations are implemented. Understanding these concepts is important for users because Bayesian analysis lends itself to custom or very complex models, and users must be able to code these themselves.
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