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Experiments
Praise for the First Edition: "If you ... want an up-to-date, definitive reference written by authors who have contributed much to this field, then this book is an essential addition to your library." —Journal of the American Statistical Association A COMPREHENSIVE REVIEW OF MODERN EXPERIMENTAL DESIGN Experiments: Planning, Analysis, and Optimization, Third Edition provides a complete discussion of modern experimental design for product and process improvement—the design and analysis of experiments and their applications for system optimization, robustness, and treatment comparison. While maintaining the same easy-to-follow style as the previous editions, this book continues to present a...
Experiments
A modern and highly innovative guide to industrial experimental design The past two decades have seen major progress in the use of statistically designed experiments for product and process improvement. In this new work, Jeff Wu and Michael Hamada, two highly recognized researchers in the field, introduce some of the newest discoveries in the design and analysis of experiments as well as their applications to system optimization, robustness, and treatment comparisons in the diverse fields of engineering, technology, agriculture, biology, and medicine. Drawing on examples from their impressive roster of industrial clients (including GM, Ford, AT&T, Lucent Technologies, and Chrysler), Wu and H...
Bayesian Reliability
Bayesian Reliability presents modern methods and techniques for analyzing reliability data from a Bayesian perspective. The adoption and application of Bayesian methods in virtually all branches of science and engineering have significantly increased over the past few decades. This increase is largely due to advances in simulation-based computational tools for implementing Bayesian methods. The authors extensively use such tools throughout this book, focusing on assessing the reliability of components and systems with particular attention to hierarchical models and models incorporating explanatory variables. Such models include failure time regression models, accelerated testing models, and ...
Mathematical Reliability: An Expository Perspective
Consideration was given to more advanced theoretical approaches and novel applications of reliability to ensure that topics having a futuristic impact were specifically included. The entries have been categorized into seven parts, each emphasizing a theme that seems poised for the future development of reliability as an academic discipline with relevance. The topics, when linked with utility theory, constitute the science base of risk analysis.
Design and Analysis of Experiments and Observational Studies using R
Introduction to Design and Analysis of Scientific Studies exposes undergraduate and graduate students to the foundations of classical experimental design and observational studies through a modern framework - The Rubin Causal Model. A causal inference framework is important in design, data collection and analysis since it provides a framework for investigators to readily evaluate study limitations and draw appropriate conclusions. R is used to implement designs and analyse the data collected. Features: Classical experimental design with an emphasis on computation using tidyverse packages in R. Applications of experimental design to clinical trials, A/B testing, and other modern examples. Discussion of the link between classical experimental design and causal inference. The role of randomization in experimental design and sampling in the big data era. Exercises with solutions. Instructor slides in RMarkdown, a new R package will be developed to be used with book, and a bookdown version of the book will be freely available. The proposed book will emphasize ethics, communication and decision making as part of design, data analysis, and statistical thinking.
Modern Statistical and Mathematical Methods in Reliability
This volume contains extended versions of 28 carefully selected and reviewed papers presented at The Fourth International Conference on Mathematical Methods in Reliability in Santa Fe, New Mexico, June 21OCo25, 2004, the leading conference in reliability research. The meeting serves as a forum for discussing fundamental issues on mathematical methods in reliability theory and its applications. A broad overview of current research activities in reliability theory and its applications is provided with coverage on reliability modeling, network and system reliability, Bayesian methods, survival analysis, degradation and maintenance modeling, and software reliability. The contributors are all leading experts in the field and include the plenary session speakers, Tim Bedford, Thierry Duchesne, Henry Wynn, Vicki Bier, Edsel Pena, Michael Hamada, and Todd Graves."
Knowledge Intensive Design Technology
Knowledge Intensive Design Technology is a collection of papers presented at the Fifth Workshop on Knowledge Intensive CAD, which was sponsored by the International Federation for Information Processing (IFIP) Working Group 5.2 and hosted by the Department of Manufacturing Engineering at the University of Malta in July 2002. The book chapters progressively take the reader through the following sequential sections; -Part One - KIC Development Approaches, -Part Two - Knowledge Systematization, -Part Three - Prototype KIC Systems. Knowledge Intensive Design Technology makes essential reading for practicing engineers/scientists involved in R&D as well as for relevant Masters and Ph.D. students. The book is also pertinent to those in industry concerned with capturing and structuring company-specific knowledge for proactive reuse to increase product development efficiency, and also to those involved in the development of CAD systems.
Industrial Data Analytics for Diagnosis and Prognosis
Discover data analytics methodologies for the diagnosis and prognosis of industrial systems under a unified random effects model In Industrial Data Analytics for Diagnosis and Prognosis - A Random Effects Modelling Approach, distinguished engineers Shiyu Zhou and Yong Chen deliver a rigorous and practical introduction to the random effects modeling approach for industrial system diagnosis and prognosis. In the book’s two parts, general statistical concepts and useful theory are described and explained, as are industrial diagnosis and prognosis methods. The accomplished authors describe and model fixed effects, random effects, and variation in univariate and multivariate datasets and cover ...
Mathematical Reviews
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Statistical Intervals
Describes statistical intervals to quantify sampling uncertainty,focusing on key application needs and recently developed methodology in an easy-to-apply format Statistical intervals provide invaluable tools for quantifying sampling uncertainty. The widely hailed first edition, published in 1991, described the use and construction of the most important statistical intervals. Particular emphasis was given to intervals—such as prediction intervals, tolerance intervals and confidence intervals on distribution quantiles—frequently needed in practice, but often neglected in introductory courses. Vastly improved computer capabilities over the past 25 years have resulted in an explosion of the ...
