Books in the Springer Texts in Statistics series

This book presents key modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, and clustering.

Suitable for self study Use real examples and real data sets that will be familiar to the audience Introduction to the bootstrap is included - this is a modern method missing in many other books

Time Series Analysis and Its Applications, presents a comprehensive treatment of both time and frequency domain methods with accompanying theory. Extensive examples illustrate solutions to climate change, monitoring a nuclear test ban treaty, evaluating the volatility of an asset, and more.

In its revised new edition, this book covers Markov chains in discrete and continuous time, Poisson processes, renewal processes, martingales and mathematical finance. Offers many examples and more than 300 carefully chosen exercises for better understanding.

This graduate-level textbook is primarily aimed at graduate students of statistics, mathematics, science, and engineering who have had an undergraduate course in statistics, an upper division course in analysis, and some acquaintance with measure theoretic probability.

This book considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response.

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The present book provides a fully self-contained introduction to the world of modern mathematical statistics, collecting the basic knowledge, concepts and findings needed for doing further research in the modern theoretical and applied statistics.

This updated new edition includes a wealth of additional material. As well as its integration of mathematical theory and numerical algorithm development, it features new chapters on topics such as the calculus of variations, integration, and block relaxation.

This book presents numerous exercises with solutions to help the reader better understand different aspects of modern statistics. It features applications with R and Matlab code that show how to practically use the methods.

This second, much enlarged edition by Lehmann and Casella of Lehmann's classic text on point estimation maintains the outlook and general style of the first edition. All of the topics are updated, while an entirely new chapter on Bayesian and hierarchical Bayesian approaches is provided, and there is much new material on simultaneous estimation.

Through its scope and depth of coverage, this book addresses the needs of the vibrant and rapidly growing engineering fields, bioengineering and biomedical engineering, while implementing software familiar to engineers.

With new chapters on asymptotic and numerical methods, as well as an appendix on the finer points of the mathematical theory, this second edition emphasizes mathematical modeling, computational techniques, and examples from the biological sciences

This book provides a self-contained review of all the relevant topics in probability theory. A software package called MAXIM, which runs on MATLAB, is made available for downloading. Vidyadhar G. Kulkarni is Professor of Operations Research at the University of North Carolina at Chapel Hill.

This textbook provides a wide-ranging introduction to the use and theory of linear models for analyzing data. The authors emphasis is on providing a unified treatment of linear models, including analysis of variance models and regression models, based on projections, orthogonality, and other vector space ideas.

This book focuses on tools and techniques for building valid regression models using real-world data. A key theme throughout the book is that it only makes sense to base inferences or conclusions on valid models.

The authors here present statistical methods and models of importance to quantitative finance and links finance theory to market practice via statistical modeling and decision making. They provide basic statistical background as well as in-depth applications.

This book covers the basic results and methods in probability theory. This new edition offers updated content, 100 additional problems for solution, and a new chapter glimpsing further topics such as stable distributions, domains of attraction and martingales.

This unique book delivers an encyclopedic treatment of classic as well as contemporary large sample theory. It deals with both statistical problems and probabilistic issues and tools. The book's detailed coverage is written in an extremely lucid style.

This comprehensive text provides the reader with a single book where they can find accounts of a number of up-to-date issues in nonparametric inference, all set out with exceptional clarity. The book's dual approach includes a mixture of methodology and theory.

Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.

An ideal text for applied statisticians needing a standalone introduction to computational Bayesian statistics, this work by a renowned authority on the subject focuses on standard models backed up by real datasets. It includes an inclusive R (CRAN) package.

A carefully written text, suitable as an introductory course for second or third year students. The main scope of the text guides students towards a critical understanding and handling of data sets together with the ensuing testing of hypotheses.

This book is in two volumes, and is intended as a text for introductory courses in probability and statistics at the second or third year university level. The likelihood ratio statistic is used to unify the material on testing, and connect it with earlier material on estimation.