Bücher der Reihe Springer Series in Statistics

Material on manifolds and locally compact groups had not yet reached the pages of multivariate books of the time and also much material about multivariate computations existed only in the journal literature or in unpublished sets oflecture notes.

This book is a treatise of risk theory with emphasis on models where the occurrence of the claims is described by more general point processes than the Poisson process, such as renewal processes, Cox processes and general stationary point processes.

The multivariate normal distribution has played a predominant role in the historical development of statistical theory, and has made its appearance in various areas of applications.

1 To the king, my lord, from your servant Balasi : 2 ... Maybe the scribe who reads to the king did not understand . Functionals extend the parameter of the assumed ideal center model to neighborhoods of this model that contain the actual distri bution.

The main topics covered include: Box-Jenkins' method, inverse autocorrelation functions, penalty function identification such as AIC, BIC techniques and Hannan and Quinn's method, instrumental regression, and a range of pattern identification methods.

Statistical modeling is a critical tool in scientific research. The authors expect this work to be of great value not just to statisticians but also to researchers and practitioners in various fields of research such as information science, computer science, engineering, bioinformatics, economics, marketing and environmental science.

Classical Extreme Value Theory-the asymptotic distributional theory for maxima of independent, identically distributed random variables-may be regarded as roughly half a century old, even though its roots reach further back into mathematical antiquity.

Topics covered include: optimal predictors for various superpopulation models, Bayes, minimax, and maximum likelihood predictors, classical and Bayesian prediction intervals, model robustness, and models with measurement errors.

Continuous time parameter Markov chains have been useful for modeling various random phenomena occurring in queueing theory, genetics, demography, epidemiology, and competing populations.

A large number of Mostellar's friends, colleagues, collaborators, and former students have contributed to the preparation of this volume in honor of his 70th birthday. It provides a critical assessment of Mosteller's professional and research contributions to the field of statistics and its applications.

The book provides insights into why some models are difficult to fit, how to combine fits over different data sets, how to improve data collection to reduce prediction variance, and how to program particular models to handle a full range of data sets.

The theory of random functions is a very important and advanced part of modem probability theory, which is very interesting from the mathematical point of view and has many practical applications.

Correlation Theory of Stationary and Related Random Functions is an elementary introduction to the most important part of the theory dealing only with the first and second moments of these functions.

This book provides a balanced, modern introduction to Bayesian and frequentist methods for regression analysis. The author discusses Frequentist and Bayesian Inferences; Linear Models; Binary Data Models; General Regression Models and Survival Models.

This book explains the theory as well as shares important applications of the Gini methodology. It demonstrates how readers may use Gini instead of variance for their research in the fields of statistics, economics, econometrics, and policy.

Updated to include the latest computational methods, this second edition explains how to use the 'gss' R package and features expanded empirical studies, a reorganized content, and a further new appendix analyzing new and controversial topics in smoothing.

This book examines sequential experimentation, including group sequential and adaptive designs of Phase II and III clinical trials. It includes recent work that provides a new class of adaptive designs which are both flexible and efficient.

This text on stochastic processes and their applications is based on a set of lectures given during the past several years at the University of California, Santa Barbara (UCSB). The concept of a simple point process on R+ is introduced in Chapter 2.

This book explores both non parametric and general statistical ideas by developing non parametric procedures in simple situations.

Kruskal published the fmt of a series of four landmark papers on measures of association for cross classifications. Only by the thoughtful choice of a measure of association can one hope to lose only the less important information and thus arrive at a satisfactory data summary.

With this new edition Springer-Verlag has republished the original edition along with my review article on multiple comparisons from the December 1977 issue of the Journal of the American Statistical Association. A few minor typographical errors in the original edition have been corrected in this new edition.

By a statistical experiment we mean the procedure of drawing a sample with the intention of making a decision. These achieve ments rested upon the fortunate fact that the foundations of probability had by then been laid bare, for it appears to be necessary that any such quantitative theory of statistics be based upon probability theory.

Part B, presenting some more extended examples of statistical analysis of data, has also the further aim of suggesting the interplay of computing and theory that must surely henceforth be typical of the develop ment of statistical science.

It covers many of the contributions made by the statisticians in the past twenty years or so towards our understanding of estimation, the Lyapunov-like index, the nonparametric regression, and many others, many of which are motivated by their dynamical system counterparts but have now acquired a distinct statistical flavor.

This book treats distributions and classical models as generalized regression models, and the result is a much broader application base for GLMs and GAMs. The book can be used in senior undergraduate or first-year postgraduate courses on GLMs or categorical data analysis and as a methodology resource for VGAM users.

This book focuses on statistical methods for the analysis of discrete failure times. Although there are a large variety of statistical methods for failure time analysis, many techniques are designed for failure times that are measured on a continuous scale.