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Focusing on Bayesian approaches and computations using analytic and simulation-based methods for inference, Time Series: Modeling, Computation, and Inference, Second Edition integrates mainstream approaches for time series modeling with significant recent developments in methodology and applications of time series analysis. It encompasses a graduate-level account of Bayesian time series modeling, analysis and forecasting, a broad range of references to state-of-the-art approaches to univariate and multivariate time series analysis, and contacts research frontiers in multivariate time series modeling and forecasting.It presents overviews of several classes of models and related methodology for inference, statistical computation for model fitting and assessment, and forecasting. It explores the connections between time- and frequency-domain approaches and develop various models and analyses using Bayesian formulations and computation, including use of computations based on Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods. It illustrates the models and methods with examples and case studies from a variety of fields, including signal processing, biomedicine, environmental science, and finance.Along with core models and methods, the book represents state-of-the art approaches to analysis and forecasting in challenging time series problems. It also demonstrates the growth of time series analysis into new application areas in recent years, and contacts recent and relevant modeling developments and research challenges.New in the second edition:Expanded on aspects of core model theory and methodology.Multiple new examples and exercises.Detailed development of dynamic factor models.Updated discussion and connections with recent and current research frontiers.

Providing a much-needed bridge between elementary statistics courses and advanced research methods courses, this text helps students grasp the fundamental assumptions and machinery behind sophisticated statistical topics, such as logistic regression, maximum likelihood, bootstrapping, nonparametrics, and Bayesian methods. The book teaches students how to properly model, think critically, and design their own studies to avoid common errors. It leads them to think differently not only about math and statistics but also about general research and the scientific method.

A practical guide to statistical methods which reflects developments in the field. It includes a chapter introducing mixed models for binary data analysis and another on methods for modelling binary data. It also includes material on modelling ordered categorical data and provides a summary of the leading software packages.

Incorporating changes in theory and highlighting various applications, this book presents a comprehensive introduction to the methods of Markov Chain Monte Carlo (MCMC) simulation technique. It incorporates the developments in MCMC, including reversible jump, slice sampling, bridge sampling, path sampling, multiple-try, and delayed rejection.

Focusing on the important role that statistical methods play in the analysis of the data collected as well as in the overall clinical trial process, this title provides an introduction to clinical trials. It features examples, exercises, and material on binary outcomes and survival analysis. It features various real examples taken from The Lancet.

Intended for those involved in complex processes in any industry, this book covers basic and more advanced techniques of data analysis. It also discusses experimental design and the so-called "Taguchi methods". Throughout the emphasis is on quality improvement and process capability.

This textbook and reference book is aimed at statisticians and scientists who would like to gain practical experience with the design and analysis of experiments, with enough theory to understand the analysis of standard and non-standard experimental design.

This publication provides an insight into modern developments in statistical methodology using examples that highlight connections between these techniques as well as their relationship to other established approaches. Illustration by simple numerical examples takes priority over abstract theory.

The first edition of this book has established itself as one of the leading references on generalized additive models (GAMs), and the only book on the topic to be introductory in nature with a wealth of practical examples and software implementation. It is self-contained, providing the necessary background in linear models, linear mixed models, and generalized linear models (GLMs), before presenting a balanced treatment of the theory and applications of GAMs and related models. The author bases his approach on a framework of penalized regression splines, and while firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. Use of R software helps explain the theory and illustrates the practical application of the methodology. Each chapter contains an extensive set of exercises, with solutions in an appendix or in the book¿s R data package gamair, to enable use as a course text or for self-study.

Rev. ed. of: Computer-aided multivariate analysis. 4th ed. c2004.

Statistical Rethinking: A Bayesian Course with Examples in R and Stan, Second Edition builds knowledge/confidence in statistical modeling. Pushes readers to perform step-by-step calculations (usually automated.) Unique, computational approach.

"This book presents the theory and practice of non-parametric statistics, with an emphasis on motivating principals. The course is a combination of traditional rank based methods and more computationally-intensive topics like density estimation, kernel smoothers in regression, and robustness. The text is aimed at MS students"--

An introduction to statistics for technology, presenting the range of statistical methods commonly used in science, social science and engineering. The mathematics is simple and straightforward; statistical concepts are explained carefully; and real-life examples are used throughout the book.

In recent years, applications of advanced stochastic processes have expanded greatly. Intended for students taking a second course in stochastic processes, this textbook presents an overview of theory with applications in engineering and science. This book covers key topics such as ergodicity, crossing problems, and extremes, and opens the doors to a selection of special topics for the teacher to expand on, like extreme value theory, filter theory, long-range dependence, and point processes, and includes many exercises and examples to illustrate the theory.

This book is a text intended for advanced undergraduates or graduate students which provides theoretical tools for analyzing and designing a large class of supervised, unsupervised, and reinforcement statistical machine learning algorithms using classical theorems from the fields of nonlinear optimization theory and mathematical statistics.

This classic textbook is suitable for a first course in the theory of statistics for students with a background in calculus, multivariate calculus, and the elements of matrix algebra.

"Preface This book is intended to have three roles and to serve three associated audiences: an introductory text on Bayesian inference starting from first principles, a graduate text on effective current approaches to Bayesian modeling and computation in statistics and related fields, and a handbook of Bayesian methods in applied statistics for general users of and researchers in applied statistics. Although introductory in its early sections, the book is definitely not elementary in the sense of a first text in statistics. The mathematics used in our book is basic probability and statistics, elementary calculus, and linear algebra. A review of probability notation is given in Chapter 1 along with a more detailed list of topics assumed to have been studied. The practical orientation of the book means that the reader's previous experience in probability, statistics, and linear algebra should ideally have included strong computational components. To write an introductory text alone would leave many readers with only a taste of the conceptual elements but no guidance for venturing into genuine practical applications, beyond those where Bayesian methods agree essentially with standard non-Bayesian analyses. On the other hand, we feel it would be a mistake to present the advanced methods without first introducing the basic concepts from our data-analytic perspective. Furthermore, due to the nature of applied statistics, a text on current Bayesian methodology would be incomplete without a variety of worked examples drawn from real applications. To avoid cluttering the main narrative, there are bibliographic notes at the end of each chapter and references at the end of the book"--

Updated and expanded, this popular text focuses on the quantitative aspects of epidemiological research. It shows students how statistical principles and techniques can help solve epidemiological problems. Along with more exercises and examples using both Stata and SAS, this third edition includes a new chapter on risk scores and clinical decision rules, a new chapter on computer-intensive methods, and new sections on binomial regression models, competing risk, information criteria, propensity scoring, and splines. Supporting materials are available on the book¿s CRC Press web page.

This book is intended as a text for a two-quarter or two-semester post-calculus introduction to probability and mathematical statistics for undergraduate students in their junior or senior year, and also for graduate students in the quantitative sciences (e.g., agriculture, computer science, ecology, economics, engineering, epidemiology, genetics, psychology, and many others). The book designed to effectively serve two different audiences (a) majors and minors in mathematics and statistics and (b) students in quantitative disciplines with the appropriate mathematical background and with a serious interest of understanding probability and statistics at the foundational level.

Assumes one-semester of calculus. "Stories" make distributions (Normal, Binomial, Poisson that are widely-used in statistics) easier to remember, understand. Many books write down formulas without explaining clearly why these particular distributions are important or how they are all connected.

This new edition of this classic title, now in its seventh edition, presents a balanced and comprehensive introduction to the theory, implementation, and practice of time series analysis.

A major tool for quality control and management, statistical process control (SPC) monitors sequential processes, such as production lines and Internet traffic, to ensure that they work stably and satisfactorily. Along with covering traditional methods, this book describes many recent SPC methods that improve upon the more established techniques. The author¿a leading researcher on SPC¿shows how these methods can handle new applications. Pseudo codes are presented for important methods and all R functions and datasets are available on the author¿s website.

This text presents a balanced account of the Bayesian and frequentist approaches to statistical inference. Along with more examples and exercises, this second edition includes new material on empirical Bayes and penalized likelihoods and their impact on regression models and offers expanded material on hypothesis testing, method of moments, bias correction, and hierarchical models. It also compares the Bayesian and frequentist schools of thought and explores procedures that lie on the border between the two.