Hilbert space methods are an alternative to the measure-theoretic definitions of random-variables. They are important in the theory of martingales and stochastic integration, as well as in interpolation and density estimation.

Consists of invited papers, from internationally recognized researchers, chosen for their quality as well as their overall unity. Describes current methods along with innovative research and presents new technologies for solving problems unique to establishment surveys. Stages of the survey process are addressed in the first five parts with cross-cutting topics in the last section.

This volume presents the basic concepts and practice of building, using and interpreting single equation dynamic regression models (also called transfer function and intervention models). The book is a companion volume to Forecasting with

MACSYMA for Statisticians introduces the basic principles and ideas of MACSYMA so that it can be used for mathematical computations, manipulations and simplifications. MACSYMA is a large computer programming system which is designed to perform a wide spectrum of mathematical computations and manipulations in symbolic as well as numerical form. It operates interactively and displays results in ordinary mathematical notation.

This is a comprehensive text and reference bringing together advances in the theory of probability and statistics and relating them to applications. The three major categories of statistical models that relate dependent variables to explanatory variables are covered: univariate regression models, multivariate regression models, and simultaneous equations models.

Treats linear regression diagnostics as a tool for application of linear regression models to real-life data. Presentation makes extensive use of examples to illustrate theory. Assesses the effect of measurement errors on the estimated coefficients, which is not accounted for in a standard least squares estimate but is important where regression coefficients are used to apportion effects due to different variables. Also assesses qualitatively and numerically the robustness of the regression fit.

Provides state-of-the-art coverage for the researcher confronted with designing and executing a simulation study using continuous multivariate distributions. Concise writing style makes the book accessible to a wide audience. Well-known multivariate distributions are described, emphasizing a few representative cases from each distribution. Coverage includes Pearson Types II and VII elliptically contoured distributions, Khintchine distributions, and the unifying class for the Burr, Pareto, and logistic distributions. Extensively illustrated--the figures are unique, attractive, and reveal very...

Reflecting more than 30 years of teaching experience in the field, this guide provides engineers with an introduction to statistics and its applicability to engineering. Examples cover a wide range of engineering applications, including both chemical engineering and semiconductors.

How to conduct clinical trials in an ethical and scientifically responsible manner

This book presents a methodology for clinical trials that produces improved health outcomes for patients while obtaining sound and unambiguous scientific data. It centers around a real-world test case--involving a treatment for hypertension after open heart surgery--and explains how to use Bayesian methods to accommodate both ethical and scientific imperatives.

The book grew out of the direct involvement in the project by a diverse group of experts in medicine, statistics, philosophy,...

Offering an introduction to the theory and methods of robust statistics at an intermediate level, this work aims to illustrate the need for robust procedures in a variety of statistical contexts, and to develop the techniques and concepts which will be useful in the future analysis of new statistical models and procedures. Emphasis is placed on the concepts of breakdown point and influence function of an estimator. Coverage is provided for the technique of expressing an estimator as a descriptive measure, from which its influence function can be easily derived.