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Asymptotic Statistics
This book is an introduction to the field of asymptotic statistics. The treatment is both practical and mathematically rigorous. In addition to most of the standard topics of an asymptotics course, including likelihood inference, M-estimation, the theory of asymptotic efficiency, U-statistics, and rank procedures, the book also presents recent research topics such as semiparametric models, the bootstrap, and empirical processes and their applications. The topics are organized from the central idea of approximation by limit experiments, which gives the book one of its unifying themes. This entails mainly the local approximation of the classical i.i.d. set up with smooth parameters by location experiments involving a single, normally distributed observation. Thus, even the standard subjects of asymptotic statistics are presented in a novel way. Suitable as a graduate or Master's level statistics text, this book will also give researchers an overview of research in asymptotic statistics.
Empirical Processes with Applications to Statistics
Originally published in 1986, this valuable reference provides a detailed treatment of limit theorems and inequalities for empirical processes of real-valued random variables; applications of the theory to censored data, spacings, rank statistics, quantiles, and many functionals of empirical processes, including a treatment of bootstrap methods; and a summary of inequalities that are useful for proving limit theorems. At the end of the Errata section, the authors have supplied references to solutions for 11 of the 19 Open Questions provided in the book's original edition. Audience: researchers in statistical theory, probability theory, biostatistics, econometrics, and computer science.
The Spurs Alphabet
A complete record of every player to have made a first team appearance for Tottenham Hotspur Football Club.
An Introduction to Mathematical Statistics
This book gives an introduction into mathematical statistics.
Heavy Tailed Functional Time Series
The goal of this thesis is to treat the temporal tail dependence and the cross-sectional tail dependence of heavy tailed functional time series. Functional time series are aimed at modelling spatio-temporal phenomena; for instance rain, temperature, pollution on a given geographical area, with temporally dependent observations. Heavy tails mean that the series can exhibit much higher spikes than with Gaussian distributions for instance. In such cases, second moments cannot be assumed to exist, violating the basic assumption in standard functional data analysis based on the sequence of autocovariance operators. As for random variables, regular variation provides the mathematical backbone for ...
Perception and Control of Self-motion
This book presents studies of self-motion by an international group of basic and applied researchers including biologists, psychologists, comparative physiologists, kinesiologists, aerospace and control engineers, physicians, and physicists. Academia is well represented and accounts for most of the applied research offered. Basic theoretical research is further represented by private research companies and also by government laboratories on both sides of the Atlantic. Researchers and students of biology, psychology, physiology, kinesiology, engineering, and physics who have an interest in self-motion -- whether it be underwater, in space, or on solid ground -- will find this volume of intere...
