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The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. «This is a nice book containing a wealth of information, much of it due to the authors. . . . If an instructor designing such a course wanted a textbook, this book would be the best choice available. . . . There are many stimulating exercises, and the book also contains an excellent index and an extensive list of references.» —Technometrics «[This] book should be read carefully by anyone who is interested in dealing with statistical models in a realistic fashion.» —American Scientist Introducing concepts, theory, and applications, Robust Statistics is accessible to a broad audience, avoiding allusions to high-powered mathematics while emphasizing ideas, heuristics, and background. The text covers the approach based on the influence function (the effect of an outlier on an estimater, for example) and related notions such as the breakdown point. It also treats the change-of-variance function, fundamental concepts and results in the framework of estimation of a single parameter, and applications to estimation of covariance matrices and regression parameters.

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A concise summary of the statistical methods used in the analysis of survival data with censoring. Emphasizes recently developed nonparametric techniques. Outlines methods in detail and illustrates them with actual data. Discusses the theory behind each method. Includes numerous worked problems and numerical exercises.

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Learn rigorous statistical methods to ensure valid clinical trials This Second Edition of the critically hailed Clinical Trials builds on the text's reputation as a straightforward and authoritative presentation of statistical methods for clinical trials. Readers are introduced to the fundamentals of design for various types of clinical trials and then skillfully guided through the complete process of planning the experiment, assembling a study cohort, assessing data, and reporting results. Throughout the process, the author alerts readers to problems that may arise during the course of the trial and provides commonsense solutions. The author bases the revisions and updates on his own classroom experience, as well as feedback from students, instructors, and medical and statistical professionals involved in clinical trials. The Second Edition greatly expands its coverage, ranging from statistical principles to controversial topics, including alternative medicine and ethics. At the same time, it offers more pragmatic advice for issues such as selecting outcomes, sample size, analysis, reporting, and handling allegations of misconduct. Readers familiar with the First Edition will discover completely new chapters, including: * Contexts for clinical trials * Statistical perspectives * Translational clinical trials * Dose-finding and dose-ranging designs Each chapter is accompanied by a summary to reinforce the key points. Revised discussion questions stimulate critical thinking and help readers understand how they can apply their newfound knowledge, and updated references are provided to direct readers to the most recent literature. This text distinguishes itself with its accessible and broad coverage of statistical design methods–the crucial building blocks of clinical trials and medical research. Readers learn to conduct clinical trials that produce valid qualitative results backed by rigorous statistical methods.

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The first systematic, book-length treatment of the subject. Begins with a general introduction and the formal mathematical background behind qualitative and quantitative robustness. Stresses concepts. Provides selected numerical algorithms for computing robust estimates, as well as convergence proofs. Tables contain quantitative robustness information for a variety of estimates.

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The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. «Books such as this that bring together, clarify, and summarize recent research can lead to a great increase of interest in the area. . . . a major achievement in describing many aspects of spatial data and discussing, with examples, different methods of analysis.» –Royal Statistical Society «Dr. Ripley’s book is an excellent survey of the spatial statistical methodology. It is very well illustrated with examples [that] give a clear view of the wide scope of the subject, the way in which techniques often have to be tailored to particular applications, and the different sorts of spatial data that arise.» –The Bulletin of the London Mathematics Society Spatial Statistics provides a comprehensive guide to the analysis of spatial data. Each chapter covers a particular data format and the associated class of problems, introducing theory, giving computational suggestions, and providing examples. Methods are illustrated by computer-drawn figures. The book serves as an introduction to this rapidly growing research area for mathematicians and statisticians, and as a reference to new computer methods for researchers in ecology, geology, archaeology, and the earth sciences.

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A completely revised and expanded edition of a classic resource In the over twenty years since the publication of the Second Edition of Order Statistics, the theories and applications of this dynamic field have changed markedly. Meeting the challenges and demands of today’s students and research community, authors H. A. David and H. N. Nagaraja return with a completely revised and updated Order Statistics, Third Edition. Chapters two through nine of this comprehensive volume deal with finite-sample theory, with individual topics grouped under distribution theory (chapters two through six) and statistical inference (chapters seven through nine). Chapters ten and eleven cover asymptotic theory for central, intermediate, and extreme order statistics, representing twice the coverage of this subject than the previous edition. New sections include: Stochastic orderings Characterizations Distribution-free prediction intervals Bootstrap estimations Moving order statistics Studentized range Ranked-set sampling Estimators of tail index The authors further explain application procedures for many data-analysis techniques and quality control. An appendix provides a guide to related tables and computer algorithms. Extensive exercise sets have been updated since the last edition. In spite of many eliminations, the total number of references has increased from 1,000 to 1,500. Expanded coverage of shortcut methods, robust estimation, life testing, reliability, L-statistics, and extreme-value theory complete this one-of-a-kind resource. Students and researchers of order statistics will appreciate this updated and thorough edition.

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A comprehensive perspective on Weibull models The literature on Weibull models is vast, disjointed, and scattered across many different journals. Weibull Models is a comprehensive guide that integrates all the different facets of Weibull models in a single volume. This book will be of great help to practitioners in reliability and other disciplines in the context of modeling data sets using Weibull models. For researchers interested in these modeling techniques, exercises at the end of each chapter define potential topics for future research. Organized into seven distinct parts, Weibull Models: Covers model analysis, parameter estimation, model validation, and application Serves as both a handbook and a research monograph. As a handbook, it classifies the different models and presents their properties. As a research monograph, it unifies the literature and presents the results in an integrated manner Intertwines theory and application Focuses on model identification prior to model parameter estimation Discusses the usefulness of the Weibull Probability plot (WPP) in the model selection to model a given data set Highlights the use of Weibull models in reliability theory Filled with in-depth analysis, Weibull Models pulls together the most relevant information on this topic to give everyone from reliability engineers to applied statisticians involved with reliability and survival analysis a clear look at what Weibull models can offer.

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Bayesian Networks, the result of the convergence of artificial intelligence with statistics, are growing in popularity. Their versatility and modelling power is now employed across a variety of fields for the purposes of analysis, simulation, prediction and diagnosis. This book provides a general introduction to Bayesian networks, defining and illustrating the basic concepts with pedagogical examples and twenty real-life case studies drawn from a range of fields including medicine, computing, natural sciences and engineering. Designed to help analysts, engineers, scientists and professionals taking part in complex decision processes to successfully implement Bayesian networks, this book equips readers with proven methods to generate, calibrate, evaluate and validate Bayesian networks. The book: Provides the tools to overcome common practical challenges such as the treatment of missing input data, interaction with experts and decision makers, determination of the optimal granularity and size of the model. Highlights the strengths of Bayesian networks whilst also presenting a discussion of their limitations. Compares Bayesian networks with other modelling techniques such as neural networks, fuzzy logic and fault trees. Describes, for ease of comparison, the main features of the major Bayesian network software packages: Netica, Hugin, Elvira and Discoverer, from the point of view of the user. Offers a historical perspective on the subject and analyses future directions for research. Written by leading experts with practical experience of applying Bayesian networks in finance, banking, medicine, robotics, civil engineering, geology, geography, genetics, forensic science, ecology, and industry, the book has much to offer both practitioners and researchers involved in statistical analysis or modelling in any of these fields.

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This is the first book-length treatment of statistical surveillance methods used in financial analysis. It contains carefully selected chapters written by specialists from both fields and strikes a balance between the financial and statistical worlds, enhancing future collaborations between the two areas, and enabling more successful prediction of financial market trends. The book discusses, in detail, schemes for different control charts and different linear and nonlinear time series models and applies methods to real data from worldwide markets, as well as including simulation studies.

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Meta Analysis: A Guide to Calibrating and Combining Statistical Evidence acts as a source of basic methods for scientists wanting to combine evidence from different experiments. The authors aim to promote a deeper understanding of the notion of statistical evidence. The book is comprised of two parts – The Handbook, and The Theory. The Handbook is a guide for combining and interpreting experimental evidence to solve standard statistical problems. This section allows someone with a rudimentary knowledge in general statistics to apply the methods. The Theory provides the motivation, theory and results of simulation experiments to justify the methodology. This is a coherent introduction to the statistical concepts required to understand the authors’ thesis that evidence in a test statistic can often be calibrated when transformed to the right scale.

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