With meta-analysis methods playing a crucial role in health research in recent years, this important and clearly-written book provides a much-needed survey of the field.
Meta-analysis provides a framework for combining the results of several clinical trials and drawing inferences about the effectiveness of medical treatments. The move towards evidence-based health care and practice is underpinned by the use of meta-analysis. This book:
* Provides a thorough criticism and an up-to-date survey of meta-analysis methods
* Emphasises the practical approach, and illustrates the...

A system for statistical computing and dynamic graphics, based on the LISP language, is described in this book, which shows how to use the system for statistical calculations and graphs. The computations supported range from summary statistics through fitting linear and nonlinear regression models to general maximum likelihood estimation and approximate Bayesian computations. Standard graphs include scatter plots, rotatable plots and scatterplot matrices. No prior knowledge of LISP is assumed; the basics are introduced as they are needed. Several chapters include extensive examples.

This text approaches the analysis of variance (ANOVA) from an exploratory point of view while retaining customary least squares fitting methods. The authors go beyond the standard steps of the ANOVA table to emphasize both the individual observations and the separate parts that the analysis produces.

Explores the application of bootstrap to problems that place unusual demands on the method. The bootstrap method, introduced by Bradley Efron in 1973, is a nonparametric technique for inferring the distribution of a statistic derived from a sample.

In recent years, random variables and stochastic processes have emerged as important factors in predicting outcomes in virtually every field of applied and social science. Ironically, according to Nicolas Bouleau and Dominique Lepingle, the presence of randomness in the model sometimes leads engineers to accept crude mathematical treatments that produce inaccurate results. The purpose of Numerical Methods for Stochastic Processes is to add greater rigor to numerical treatment of stochastic processes so that they produce results that can be relied upon when making decisions and assessing...

The subject of time series is of considerable interest, especially among researchers in econometrics, engineering, and the natural sciences. As part of the prestigious Wiley Series in Probability and Statistics, this book provides a lucid introduction to the field and, in this new Second Edition, covers the important advances of recent years, including nonstationary models, nonlinear estimation, multivariate models, state space representations, and empirical model identification. New sections have also been added on the Wold decomposition, partial autocorrelation, long memory processes, and...

Combines recent developments in resampling technology (including the bootstrap) with new methods for multiple testing that are easy to use, convenient to report and widely applicable. Software from SAS Institute is available to execute many of the methods and programming is straightforward for other applications. Explains how to summarize results using adjusted p-values which do not necessitate cumbersome table look-ups. Demonstrates how to incorporate logical constraints among hypotheses, further improving power.

Statistical Factor Analysis and Related Methods Theory and Applications In bridging the gap between the mathematical and statistical theory of factor analysis, this new work represents the first unified treatment of the theory and practice of factor analysis and latent variable models. It focuses on such areas as:
* The classical principal components model and sample-population inference
* Several extensions and modifications of principal components, including Q and three-mode analysis and principal components in the complex domain
* Maximum likelihood and weighted factor...

Sequential Stochastic Optimization provides mathematicians and applied researchers with a well-developed framework in which stochastic optimization problems can be formulated and solved. Offering much material that is either new or has never before appeared in book form, it lucidly presents a unified theory of optimal stopping and optimal sequential control of stochastic processes. This book has been carefully organized so that little prior knowledge of the subject is assumed; its only prerequisites are a standard graduate course in probability theory and some familiarity with...

A discrete event system (DES) is an abstraction of many engineering and service systems characterized by event-driven dynamics. Examples include computer systems, communication networks, airports and highways. The evolution of DES is governed by events, typically occurring at random time periods. Developing new mathematical tools to study discrete event operations research, this study discusses the two lines of investigation in DES research that have emerged: logical/qualitative issues and temporal/quantitative analysis.