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In this well written book, the authors treat the fundamental question of response-adaptive randomization as dealing with the trade-off between minimizing the expected number of treatment failures and maximizing the power of inferential tests.

A fascinating investigation into the foundations of statistical inference This publication examines the distinct philosophical foundations of different statistical modes of parametric inference.

Visual statistics accomplishes the goal of bringing the most complex and advanced statistical methods within the reach of those with little statistical training by using animated graphics of the data. This text shows how to make dynamic visualizations tht are fully interactive and respond instantly to the user's nudges and prods.

A comprehensive text and reference bringing together advances in the theory of probability and statistics and relating them to applications.

This book devoted to sequential estimation presents the advances of the past fifteen years including those in the areas of three-stage accelerated sequential sampling procedures.

This volume treats linear regression diagnostics as a tool for the application of linear regression models to real-life data. The presentation makes extensive use of examples to illustrate theory.

This book provides state-of-the-art coverage for the researcher confronted with designing and executing a simulation study using continuous multivariate distributions. The concise writing style makes the book accessible to a wide audience.

Reflecting more than 30 years of teaching experience in the field, this guide provides engineers with an introduction to statistics and its applicability to engineering. Examples cover a wide range of engineering applications, including both chemical engineering and semiconductors.

This monograph uses Bayesian statistical methods to explain the nature and methodology of clinical trials.

An introduction to the theory and methods of robust statistics, which aims to illustrate the need for robust procedures in a variety of statistical contexts, and to develop the techniques and concepts useful in the analysis of new statistical models and procedures.

An overview of the survey method of statistical analysis, which explores three main areas of nonsampling survey error: non-response in obtaining data from sample members, problems with the sampling frame and inadequacies in the process of obtaining survey measures from respondents.

A balanced presentation of both theoretical and applied material with numerous problem sets to illustrate important concepts. Demonstrates the use of computers and calculators to facilitate problem solving, as well as numerous applications to illustrate basic theory.

- It reveals the interrelationships between multiple variables and features of the underlying conditional independence. - It covers conditional independence, several types of independence graphs, Gaussian models, issues in model selection, regression and decomposition. - Many numerical examples and exercises with solutions are included.

Based on the proceedings of a conference on Influence Diagrams for Decision Analysis, Inference and Prediction held at the University of California at Berkeley in May of 1988, this is the first book devoted to the subject.

Presents a system of multivariate analysis techniques in cases where statistical data may be of different measurement levels such as nominal, ordinal or interval. It covers methods of studying the stability of these techniques, including resampling by the bootstrap and jackknife and discusses sensitivity analysis through first-order approximations.

An exposition of fractals, shape and form, and point processes, which analyzes the current theoretical information. The organization of the text is such that each part can be read independently. Many case studies and true examples are used to illustrate the text.

A number of eminent experts on Clinical Trials, Epidemiology, Survival Analysis, and Genomics/Proteomics have contributed 30 carefully prepared and peer-reviewed articles to this book. Within the four sections, the articles have been organized so as to make the thematic transition between them as smooth as possible.

Recent books in the Wiley Series in Probability and Statistics Editors Vic Barnett J. Stuart Hunter David W. Scott Geoffrey S. Watson Ralph A. Bradley Joseph B. Kadane Adrian F.M. Smith Nicholas I. Fisher David G. Kendall Jozef L.

Pioneered by David Kendall, the statistical theory of shape is an emerging area generating considerable interest for statisticians, engineers, and computer scientists. Co--written by Dr. Kendall, this volume presents a coherent theory of shape developed from Kendalla s own approach known as static and kinematic theory.

"Hajek was undoubtedly a statistician of enormous power who, in his relatively short life, contributed fundamental results over a wide range of topics. " V. Barnett, University of Nottingham. Hajeka s writings in statistics are not only seminal but form a powerful unified body of theory.

Statistical inference is the process of drawing conclusions based upon the available data on the measurement of uncertainty of a defined event. It allows one to draw a conclusion or a generalization from the available data. , i.e. if there is smoke there is a good probability there is a fire.

This volume describes the algebra of matrices and shows how to apply them to today's problems in applied economics. The first section covers the essentials of matrices while the second section concentrates on major topics in applied economics such as regression, linear programming, and time series.

Focuses on latent class analysis (LCA) and latent transition analysis (LTA) with a comprehensive treatment of longitudinal latent class models. This book includes examples that enable the reader to acquire a conceptual and technical understanding and to apply techniques to address empirical research questions.

Maintaining the same nontechnical approach as its acclaimed predecessor, this second edition of Generalized Linear Models is now thoroughly extended to include the latest developments in the field, the most relevant computational approaches, and the most relevant examples from the fields of engineering and physical sciences.

Bayesian networks have found application in a number of fields, including risk analysis, consumer help desks, tissue pathology, pattern recognition, credit assessment, computer network diagnosis, and artificial intelligence. Bayesian Networks is a self-contained introduction to the theory and applications of Bayesian networks.

Random Data provides first-rate, practical tools for dynamic data and statistical methods for engineering problems. This revised bestseller presents the latest developed procedures and a complete rewrite of the Fast Fourier Transforms of applied fields. Plus, this resource includes a new chapter on frequency domain techniques.

Statistical science s first coordinated manual of methods for analyzing ordered categorical data, now fully revised and updated, continues to present applications and case studies in fields as diverse as sociology, public health, ecology, marketing, and pharmacy.

Approaches the analysis of variance (ANOVA) from an exploratory viewpoint while retaining customary least squares fitting methods. The authors emphasize both individual observations and the separate sections that ANOVA analysis produces.

Classification rules can be defined as objective, formal methods used for statistical classification. This text presents the central issues and placing particular emphasis on comparison, assessment and how to match method to application.

Written by well-known, award-winning authors, this is the first book to focus on high-dimensional data analysis while presenting real-world applications and research material.