Bøger i Springer Series in Statistics serien

This book describes methods for designing and analyzing experiments conducted using computer code in lieu of a physical experiment. It discusses how to select the values of the factors at which to run the code (the design of the computer experiment).

A principal feature of smoothing, the averaging of data points in a prescribed neighborhood, is not really practicable in dimensions greater than three if we have just one hundred data points.

This book is the reference on indirect sampling and the generalised weight share method. In addition to the underlying theory, the book presents different possible applications that drive its interest. The reader will find in this book the answer to questions that come, inevitably, when working in a context of indirect sampling.

This book is the first to offer a systematic presentation of the Bayesian perspective of finite mixture modeling, showing how finite mixture and Markov switching models are formulated, what structures they imply on the data, their potential uses, and how they are estimated.

This book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models. It presents an up-to-date account of theory and methods in analysis of these models as well as their applications in various fields.

This book provides a thorough introduction to the most important topics in data mining and machine learning. All the topics covered have undergone rapid development and this treatment offers a modern perspective emphasizing the most recent contributions.

Advanced Statistics provides a rigorous development of statistics that emphasizes the definition and study of numerical measures that describe population variables. The volumes are intended for use by graduate students in statistics and professional statisticians, although no specific prior knowledge of statistics is assumed.

This book provides an up-to-date treatment of the Monte Carlo method and develops a common framework under which various Monte Carlo techniques can be "standardized" and compared. It can be used as a textbook for a graduate-level course on Monte Carlo methods.

This volume provides a theoretical treatment of a wide range of goodness-of-fit statistical methods. By comparing techniques for both one-sample and k-sample problems, the text offers a wider perspective that considers graphic and estimation parameters, and emphasizes their theoretical similarities.

Key spatial models for three types of data are discussed in this overview of spatial modeling and statistics. Real-world applications illustrate the concepts and theories described, in addition to probabilistic properties and related statistical methods.

At the same time it shows how functional data can be studied through parameter-free statistical ideas, and offers an original presentation of new nonparametric statistical methods for functional data analysis.

Quasi-Monte Carlo methods have become an increasingly popular alternative to Monte Carlo methods over the last two decades. This book presents all of the essential tools for using quasi-Monte Carlo sampling on practical problems, especially in finance.

This book provides an up-to-date treatment of the foundations common to the statistical analysis of network data across the disciplines. The material is organized according to a statistical taxonomy, although the presentation balances concepts and mathematics.

By giving a detailed account of bootstrap methods and their properties for dependent data, this book provides illustrative numerical examples throughout.

This book is concerned with data in which the observations are independent and in which the response is multivariate. Companion book to Robust Diagnostic Regression Analysis (ISBN 0-387-95017) published by Springer in 2000.

In general terms, the shape of an object, data set, or image can be de fined as the total of all information that is invariant under translations, rotations, and isotropic rescalings.

Graphs are used to understand the relationship between a regression model and the data to which it is fitted. As well as illustrating new procedures, the authors develop the theory of the models used, particularly for generalized linear models.

In establishing a framework for dealing with uncertainties in software engineering, and for using quantitative measures in related decision-making, this text puts into perspective the large body of work having statistical content that is relevant to software engineering.

Deals with the area of comparative statistical inference. This title provides an overview of the monograph's highlights and a discussion of areas and problems in need of further research.

Although there are substantial data and plausibility arguments suppo- ing many conditions as contributory components of pathways to illness and disease end points, we have, historically, lacked an e?ective method- ogy for identifying the structure of the full pathways.

This book is intended for students and practitioners who have had a calculus-based statistics course and who have an interest in safety considerations such as reliability, strength, and duration-of-load or service life.

The book presents in a rigorous and thorough manner the main elements of Charles Manski's research on partial identification of probability distributions. The approach to inference that runs throughout the book is deliberately conservative and thoroughly nonparametric.

Previous edition sold over 1400 copies worldwide. This new edition includes many more real-world illustrations from biology, business, clinical trials, economics, geology, law, medicine, social science and engineering along with twice the number of exercises.

Over the last few decades, important progresses in the methods of sampling have been achieved. Forty-six sampling methods are described in the framework of general theory. The algorithms are described rigorously, which allows implementing directly the described methods.

Now available in paperback, this book is organized in a way that emphasizes both the theory and applications of the various variance estimating techniques. It applies to large, complex surveys; and to provide an easy reference for the survey researcher who is faced with the problem of estimating variances for real survey data.

This book provides a theoretical foundation for analysis of discrete data such as count and binary data in the longitudinal setup. It presents differences between the familial and longitudinal correlation models, and illustrations of real life data analysis.

The first edition was released in 1996 and has sold close to 2200 copies. Provides an up-to-date comprehensive treatment of MDS, a statistical technique used to analyze the structure of similarity or dissimilarity data in multidimensional space. The authors have added three chapters and exercise sets.

One of the best known statisticians of the 20th century, Frederick Mosteller has inspired numerous statisticians and other scientists by his creative approach to statistics and its applications. It is hoped that sharing these writings with a new generation of researchers will inspire them to build upon his insights and efforts.

This is the second edition of the comprehensive treatment of statistical inference using permutation techniques. This updated version places increased emphasis on the use of alternative permutation statistical tests based on metric Euclidean distance functions.