Bücher der Reihe Springer Texts in Statistics

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.

Addressing the most challenging issues faced by financial engineers, this book shows how sophisticated mathematics and modern statistical techniques can be used in concrete financial problems. Includes practical examples solved in the R computing environment.

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.

Based on the author's extensive experience in both statistics and education, this book imparts the basics of designing and carrying out Bayesian analyses, and interpreting and communicating the results. Demonstrates applications to real-world data.

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 is a text (divided into two volumes) for a two semester course in Mathematical Statistics at the Senior/Graduate level. Volume II: Statistical Inference is designed for the second semester and contains a rigorous introduction to Mathematical Statistics, from random samples to asymptotic theory of statistical inference.

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

Statistical design is one of the fundamentals of our subject, being at the core of the growth of statistics during the previous century. This book covers the basic theoretical underpinnings. It describes the principles that drive good designs and good statistics.

This book offers a comprehensive guide to large sample techniques in statistics. More importantly, it focuses on thinking skills rather than just what formulae to use; it provides motivations, and intuition, rather than detailed proofs.

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 book covers inequalities, characteristic functions and convergence, the law of large numbers, the central limit theorem and the law of the iterated logarithm. This revised edition updates core material, and offers scores of new problems and exercises.

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.

An up-to-date, rigorous, and lucid treatment of the theory, methods, and applications of regression analysis, and thus ideally suited for those interested in the theory as well as those whose interests lie primarily with applications.

This textbook presents the design and analysis of experiments that comprise the aspects of classical theory for continuous response, modern procedures for categorical response, and especially for correlated categorical response.

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.

An easy to read survey of data analysis, linear regression models and analysis of variance. The extensive development of the linear model includes the use of the linear model approach to analysis of variance provides a strong link to statistical software packages, and is complemented by a thorough overview of theory.

A Second Course in Statistics The past decade has seen a tremendous increase in the use of statistical data analysis and in the availability of both computers and statistical software.

Develops the theory of probability from axioms on the expectation functional rather than on probability measure, demonstrates that the standard theory unrolls more naturally and economically this way, and also demonstrates that applications of real interest can be addressed almost immediately.

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 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.

Special features include: - A comprehensive treatment of the law of the iterated logarithm - The Marcinklewicz-Zygmund inequality, its extension to martingales and applications thereof - Development and applications of the second moment analogue of Walds equation - Limit theorems for martingale arrays;

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.

This book introduces several topics related to linear model theory, including: multivariate linear models, discriminant analysis, principal components, factor analysis, time series in both the frequency and time domains, and spatial data analysis.

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.