The analysis of infectious disease data requires separate attention because standard methods of statistical inference cannot be applied directly. In this book these difficulties are overcome by making appropriate model assumptions.

The likelihood plays a key role in both introducing general notions of statistical theory, and in developing specific methods. This book introduces likelihood-based statistical theory and related methods from a classical viewpoint, and demonstrates how the main body of currently used statistical techniques can be generated from a few key concepts, in particular the likelihood.

Data-analytic approaches to regression problems, arising from many scientific disciplines are described in this text. The aim of these nonparametric methods is to relax assumptions on the form of a regression function, and to let data search for a suitable function that describes the data well. The use of these nonparametric functions with parametric techniques can yield very powerful data anlysis tools.

Offers the first unified collection of recent theoretical advances and applications in simulation-based inference for spatial point processes Devotes considerable attention to different facets of approximate likelihood inference and simulation-based Bayesian inference. Discusses perfect simulation procedures-one of the most exciting new development sin MCMC Includes numerous illuminating examples and illustrations Spatial point processes play a fundamental role in spatial statistics and today they are a very active area of research with many new and emerging applications. Although published...

Setting out the principles of stereology from a statistical viewpoint, this book focuses on both basic theory and practical implications. The authors discuss ways to effectively communicate statistical issues to clients, draw attention to common methodological errors, and provide references to essential literature.
The first full text on design-based stereology opens with a review of classical and modern stereology, followed by a treatment of mathematical foundations and then on to core techniques. The final chapters discuss implementing techniques in practical sampling designs, summarize...

There are a wide range of applications for Gaussian Markov Random Fields (GMRFs), from structural time-series analysis to the analysis of longitudinal and survival data, spatio-temporal models, graphical models, and semi-parametric statistics. This book provides various case studies that illustrate the use of GMRFs in complex hierarchical models."

The first statistics book devoted to spatio-temporal models, Statistical Methods for Spatio-Temporal Systems presents current statistical research issues on spatio-temporal data modeling that will promote advances in research and a greater understanding between the mechanistic and the statistical modeling communities. The book describes recent advances and presents a variety of statistical methods, including likelihood-based, nonparametric smoothing, spectral, Fourier, wavelet, and Markov chain Monte Carlo. The methods are illustrated with color images as well as real-world examples, case...

It's been over a decade since the first edition of Measurement Error in Nonlinear Models splashed onto the scene, and research in the field has certainly not cooled in the interim. In fact, quite the opposite has occurred. As a result, Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition has been revamped and extensively updated to offer the most comprehensive and up-to-date survey of measurement error models currently available.

What's new in the Second Edition?

. Greatly expanded discussion and applications of Bayesian computation via...

Set-Indexed Martingales offers a unique, comprehensive development of a general theory of Martingales indexed by a family of sets. The authors establish-for the first time-an appropriate framework that provides a suitable structure for a theory of Martingales with enough generality to include many interesting examples.
Developed from first principles, the theory brings together the theories of Martingales with a directed index set and set-indexed stochastic processes. Part One presents several classical concepts extended to this setting, including: stopping, predictability, Doob-Meyer...

This is a classic book on Queues. First published in 1961 it is clearly and concisely introduces the theory of queueing systems and is still just as relevant today. The monograph is aimed at both students and operational research workers concerned with the practical investigations of queueing, although almost every statistician will find its contents of interest.