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Kosorok's brilliant text provides a self-contained introduction to empirical processes and semiparametric inference. These powerful research techniques are surprisingly useful for developing methods of statistical inference for complex models and in understanding the properties of such methods.

Developed from lecture notes and ready to be used for a course on the graduate level, this concise text aims to introduce the fundamental concepts of nonparametric estimation theory while maintaining the exposition suitable for a first approach in the field.

This book summarizes current knowledge of the theory of estimation for semiparametric models with missing data, applying modern methods to missing, censored, and coarsened data with the goal of deriving estimators that are as robust and efficient as possible.

A treatment of the problems of inference associated with experiments in science, with the emphasis on techniques for dividing the sample information into various parts, such that the diverse problems of inference that arise from repeatable experiments may be addressed.

This book introduces basic concepts, main results and widely-applied mathematical tools in the spectral analysis of large dimensional random matrices. This updated edition includes two new chapters and summaries from the field of random matrix theory.

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.

This book explores weak convergence theory and empirical processes and their applications to many applications in statistics. Part two offers the theory of empirical processes in a form accessible to statisticians and probabilists.

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

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.

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 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.

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.

This text emphasizes the main ideas underlying a variety of nonparametric and semiparametric methods. This edition contains over one hundred pages of new material as well as empirical examples to illustrate the methods presented.

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.