General

Rank-Based Methods for Shrinkage and Selection A practical and hands-on guide to the theory and methodology of statistical estimation based on rank Robust statistics is an important field in contemporary mathematics and applied statistical methods...

The world is awash in data. This volume of data will continue to increase. In the pharmaceutical industry, much of this data explosion has happened around biomarker data. Great statisticians are needed to derive understanding from these data. This book...

Cybersecurity Analytics is for the cybersecurity student and professional who wants to learn data science techniques critical for tackling cybersecurity challenges, and for the data science student and professional who wants to learn about cybersecurity...

Health economics is concerned with the study of the cost-effectiveness of health care interventions. This book provides an overview of Bayesian methods for the analysis of health economic data. After an introduction to the basic economic concepts and...

Modeling spatial and spatio-temporal continuous processes is an important and challenging problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA describes in detail the stochastic partial...

A much-needed reference on survey sampling and its applications that presents the latest advances in the field Seeking to show that sampling theory is a living discipline with a very broad scope, this book examines the modern development of the theory of...

Martingale Methods in Statistics provides a unique introduction to statistics of stochastic processes written with the author’s strong desire to present what is not available in other textbooks. While the author chooses to omit the well-known proofs of...

Adopting a unifying theme based on maximum statistics, Multiple Comparisons Using R describes the common underlying theory of multiple comparison procedures through numerous examples. It also presents a detailed description of available software...

This textbook takes a strategic approach to the broad-reaching subject of experimental design by identifying the objectives behind an experiment and teaching practical considerations that govern design and implementation, concepts that serve as the basis...

This book takes a fresh look at the popular and well-established method of maximum likelihood for statistical estimation and inference. It begins with an intuitive introduction to the concepts and background of likelihood, and moves through to the latest...

a very useful resource for courses in nonparametric statistics in which the emphasis is on applications rather than on theory. It also deserves a place in libraries of all institutions where introductory statistics courses are taught." CHOICE This...

Wide-Ranging Coverage of Parametric Modeling in Linear and Nonlinear Mixed Effects ModelsMixed Effects Models for the Population Approach: Models, Tasks, Methods and Tools presents a rigorous framework for describing, implementing, and using mixed...

This textbook offers a compact introductory course on Malliavin calculus, an active and powerful area of research. It covers recent applications, including density formulas, regularity of probability laws, central and non-central limit theorems for...

The complete guide to statistical modelling with GENSTAT Focusing on solving practical problems and using real datasets collected during research of various sorts, Statistical Modelling Using GENSTAT emphasizes developing and understanding statistical...

A How-To Guide for Conducting Common Fisheries-Related Analyses in RIntroductory Fisheries Analyses with R provides detailed instructions on performing basic fisheries stock assessment analyses in the R environment. Accessible to practicing fisheries...

Presents a novel approach to the statistical design of experiments, offering a simple way to specify and evaluate all possible designs without restrictions to classes of named designs. The work also presents a scientific design method from the...

Originally published in 1973. This book presents a valid mode of reasoning that is different to mathematical probability. This inductive logic is investigated in terms of scientific investigation. The author presents his criteria of adequacy for...

This is a graduate-level introduction to the theory of Boolean functions, an exciting area lying on the border of probability theory, discrete mathematics, analysis, and theoretical computer science. Certain functions are highly sensitive to noise; this...

Levy processes are rich mathematical objects and constitute perhaps the most basic class of stochastic processes with a continuous time parameter. This book is intended to provide the reader with comprehensive basic knowledge of Levy processes, and at...

This is the first text to examine the use of statistical methods in forensic science and bayesian statistics in combination. The book is split into two parts: Part One concentrates on the philosophies of statistical inference. Chapter One examines the...

Modern computer-intensive statistical methods play a key role in solving many problems across a wide range of scientific disciplines. Like its bestselling predecessors, the fourth edition of Randomization, Bootstrap and Monte Carlo Methods in Biology...

Meta-analysis is the application of statistics to combine results from multiple studies and draw appropriate inferences. Its use and importance have exploded over the last 25 years as the need for a robust evidence base has become clear in many...

This book presents the most important ideas behind Bayes' Rule in a form suitable for the general reader. It is written without formulae because they are not necessary; the ability to add and multiply is all that is needed. As well as showing in full the...

Percolation theory was initiated some fifty years ago as a mathematical framework for the study of random physical processes such as flow through a disordered porous medium. It has proved to be a remarkably rich theory, with applications beyond natural...

Comprising the major theorems of probability theory and the measure theoretical foundations of the subject, the main topics treated here are independence, interchangeability, and martingales. Particular emphasis is placed upon stopping times, both as...

Generalized linear models (GLMs) extend linear regression to models with a non-Gaussian, or even discrete, response. GLM theory is predicated on the exponential family of distributions-a class so rich that it includes the commonly used logit, probit, and...

Box and Jenkins (1970) made the idea of obtaining a stationary time series by differencing the given, possibly nonstationary, time series popular. Numerous time series in economics are found to have this property. Subsequently, Granger and Joyeux (1980)...

This book provides a guide to the elements of simulation in statistics, which will include developments and which may be used either as a teaching text or as a source of reference. It is widely used for teaching statistics in schools.Reviews"This book...

This book arose out of two graduate courses that the authors have taught duringthepastseveralyears;the?rstonebeingonmeasuretheoryfollowed by the second one on advanced probability theory. The traditional approach to a ?rst course in measure theory, such...

Acclaim for "In today's world, 'innumeracy' is an even greater danger than illiteracy, and is perhaps even more common. Advertisers and politicians exploit it; intellectuals (self-styled) even flaunt it. I hope that this wise and witty book will...

An important factor that affects the duration, complexity and cost of a clinical trial is the endpoint used to study the treatment's efficacy. When a true endpoint is difficult to use because of such factors as long follow-up times or prohibitive cost,...

Revised, expanded, and updated, this second edition of Statistics for Environmental Science and Management is that rare animal, a resource that works well as a text for graduate courses and a reference for appropriate statistical approaches to specific...

This important book provides an up-to-date comprehensive and down-to-earth survey of the theory and practice of extreme value distributions - one of the most prominent success stories of modern applied probability and statistics. Originated by E J Gumbel...

An extensive update to a classic text Stochastic geometry and spatial statistics play a fundamental role in many modern branches of physics, materials sciences, engineering, biology and environmental sciences. They offer successful models for the...

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the...

Model-based Geostatistics for Global Public Health: Methods and Applications provides an introductory account of model-based geostatistics, its implementation in open-source software and its application in public health research. In the public health...

Modeling spatial and spatio-temporal continuous processes is an important and challenging problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA describes in detail the stochastic partial...

Tools to improve decision making in an imperfect world This publication provides readers with a thorough understanding of Bayesian analysis that is grounded in the theory of inference and optimal decision making. Contemporary Bayesian Econometrics and...

Contingency tables arise in diverse fields, including life sciences, education, social and political sciences, notably market research and opinion surveys. Their analysis plays an essential role in gaining insight into structures of the quantities under...

An accessible book on sampling techniques with emphasis on and illustrations from surveys of human populations. Explains how to design and execute valid samples of moderate dimensions and difficulty, avoid selection biases and how to become more adept at...