Books in the Springer Series in Statistics series

Bayesian statistics is one of the active research areas in statistics. This book provides the theoretical background behind the important development, Markov chain Monte Carlos methods.

The book is aimed at applied statisticians, graduate students of statistics, and students and researchers with a strong interest in statistics and data analysis. This second edition is extensively revised, especially those sections relating with Bayesian concepts.

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.

This text is concerned with Bayesian learning, inference and forecasting in dynamic environments.

This volume is the first book-length treatment of model-based geostatistics. The text is expository, emphasizing statistical methods and applications rather than the underlying mathematical theory. It features analyses of datasets from a range of scientific contexts.

The theory of inequalities has applications in virtually every branch of mathematics. This revised and expanded edition of a classic work on inequalities will be of interest to statisticians, probabilists, and mathematicians.

Here is a systematic account of linear time series models and their application to the modeling and prediction of data collected sequentially in time. It details techniques for handling data and offers a thorough understanding of their mathematical basis.

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 reference text presents comprehensive coverage of the various notions of stochastic orderings, their closure properties, and their applications. It is an ideal reference for anyone interested in decision making under uncertainty.

This book covers a highly relevant topic that is of wide interest, especially in finance, engineering and computational biology. With an emphasis on the practical implementation of the simulation and estimation methods presented, the text will be useful to practitioners with minimal mathematical background.

A book in the "Springer Series" in Statistics.

The primary aim of this book is to provide modern statistical techniques and theory for stochastic processes. A wide variety of stochastic processes, including non-Gaussian linear processes, long-memory processes, nonlinear processes, non-ergodic processes and diffusion processes are described.

The linear mixed model has become the main parametric tool for the analysis of continuous longitudinal data, as the authors discussed in their 2000 book.

In this new edition the author has added substantial material on Bayesian analysis, including lengthy new sections on such important topics as empirical and hierarchical Bayes analysis, Bayesian calculation, Bayesian communication, and group decision making.

The principal focus here is on autoregressive moving average models and analogous random fields, with probabilistic and statistical questions also being discussed.

In many statistical applications, scientists have to analyze the occurrence of observed clusters of events in time or space. Scientists are especially interested in determining whether an observed cluster of events has occurred by chance if it is assumed that the events are distributed independently and uniformly over time or space.

This book is a comprehensive treatment of inference for hidden Markov models, including both algorithms and statistical theory. The book builds on recent developments, both at the foundational level and the computational level, to present a self-contained view.

This book provides a systematic in-depth analysis of nonparametric regression with random design. It covers almost all known estimates. The emphasis is on distribution-free properties of the estimates.

The resampling methods replace theoreti cal derivations required in applying traditional methods (such as substitu tion and linearization) in statistical analysis by repeatedly resampling the original data and making inferences from the resamples.

All other articles (including those of his contributions to Mathematical Reviews which go beyond a simple reporting of contents of articles) have been reproduced as they appeared, together with annotations and corrections made by Wassily on some private copies of his papers.

The first edition of this book was the first comprehensive text written solely on principal component analysis. The second edition updates and substantially expands the original version, and is once again the definitive text on the subject. Its length is nearly double that of the first edition.

The examples are analyzed with the free software nls2 updated to deal with the new models included in the second edition. Several additional tools are included in the package for calculating confidence regions for functions of parameters or calibration intervals, using classical methodology or bootstrap.

This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Next to model formulation, this edition puts major emphasis on exploratory data analysis for all aspects of the model, such as the marginal model, subject-specific profiles, and residual covariance structure.

Intercropping is an area of research for which there is a desperate need, both in developing countries where people are rapidly depleting scarce resources and still starving, and in developed countries, where more ecologically and economically sound ways of feeding ourselves must be developed.

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.

The pioneering research of Hirotugu Akaike has an international reputation for profoundly affecting how data and time series are analyzed and modelled and is highly regarded by the statistical and technological communities of Japan and the world.

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.

A more accurate title for this book might be: An Exposition of Selected Parts of Empirical Process Theory, With Related Interesting Facts About Weak Convergence, and Applications to Mathematical Statistics. The material is somewhat arbitrarily divided into results used to prove consistency theorems and results used to prove central limit theorems.

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.

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.