ISBN: 0-471-39104-2
Hardcover
673 Pages
November 04, 2002
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Praise for the First Edition
"An enticing approach to the subject. . . . Students
contemplating a career in statistics will acquire a valuable
understanding of the underlying structure of statistical theory.
. . statisticians should consider purchasing it as an additional
reference on advanced calculus."
?Journal of the American Statistical Association
"This book is indeed a pleasure to read. It is simple to
understand what the author is attempting to accomplish, and to
follow him as he proceeds. . . . I would highly recommend the
book for one’s personal collection or suggest your librarian
purchase a copy."
?Journal of the Operational Research Society
Knowledge of advanced calculus has become imperative to the
understanding of the recent advances in statistical methodology.
The First Edition of Advanced Calculus with Applications in
Statistics has served as a reliable resource for both practicing
statisticians and students alike. In light of the tremendous
growth of the field of statistics since the book’s publication,
Andre Khuri has reexamined his popular work and substantially
expanded it to provide the most up-to-date and comprehensive
coverage of the subject.
Retaining the original’s much-appreciated application-oriented
approach, Advanced Calculus with Applications in Statistics,
Second Edition supplies a rigorous introduction to the central
themes of advanced calculus suitable for both statisticians and
mathematicians alike. The Second Edition adds significant new
material on:
Basic topological concepts
Orthogonal polynomials
Fourier series
Approximation of integrals
Solutions to selected exercises
The volume’s user-friendly text is notable for its end-of-chapter
applications, designed to be flexible enough for both
statisticians and mathematicians. Its well thought-out solutions
to exercises encourage independent study and reinforce mastery of
the content. Any statistician, mathematician, or student wishing
to master advanced calculus and its applications in statistics
will find this new edition a welcome resource.
ISBN: 0-471-41373-9
Hardcover
696 Pages
January 2003
Table of Contents Author Information
Introducing a groundbreaking companion book to a bestselling
reliability text
Reliability is one of the most important characteristics defining
the quality of a product or system, both for the manufacturer and
the purchaser. One achieves high reliability through careful
monitoring of design, materials and other input, production,
quality assurance efforts, ongoing maintenance, and a variety of
related decisions and activities. All of these factors must be
considered in determining the costs of production, purchase, and
ownership of a product.
Case Studies in Reliability and Maintenance serves as a valuable
addition to the current literature on the subject of reliability
by bridging the gap between theory and application. Conceived
during the preparation of the editors’ earlier work,
Reliability: Modeling, Prediction, and Optimization (Wiley, 2000),
this new volume features twenty-six actual case studies written
by top experts in their fields, each illustrating exactly how
reliability models are applied.
A valuable companion book to Reliability: Modeling, Prediction,
and Optimization, or any other textbook on the subject, the book
features:
Case studies from fields such as aerospace, automotive, mining,
electronics, power plants, dikes, computer software, weapons,
photocopiers, industrial furnaces, granite building cladding,
chemistry, and aircraft engines
A logical organization according to the life cycle of a product
or system
A unified format of discussion enhanced by tools, techniques, and
models for drawing one’s own conclusions
Pertinent exercises for reinforcement of ideas
Of equal value to both students of reliability theory as well as
professionals in industry, Case Studies in Reliability and
Maintenance should be required reading for anyone seeking to
understand how reliability and maintenance issues can be
addressed and resolved in the real world.
ISBN: 0-471-36997-7
Hardcover
552 Pages
February 2003
Table of Contents
Preface.
Introduction.
Functions of Survival Time.
Examples of Survival Data Analysis.
Nonparametric Methods of Estimating Survival Functions.
Nonparametric Methods for Comparing Survival Distributions.
Some Well-Known Parametric Survival Distribution and Their
Applications.
Estimation Procedures for Parametric Survival Distributions
Without Covariates.
Graphical Methods in Survival Distribution Fitting.
Tests of Goodness-of-Fit and Distributon Selection.
Parametric Methods for Comparing Two Survival Distributions.
Parametric Methods for Regression Model Fitting and
Identification of Prognostic Factors.
Identification of Prognostic Factors Related to Survival Time:
Cox Proportional Hazards Model.
Identification of Prognostic Factors Related to Survival Time:
Non-Proportional Hazards Models.
Identification of Rich Factors Related to Dichotomous or
Polychotomous Outcomes.
Appendix A: The Newton-Raphson Method.
Appendix B: Statistical Tables.
References.
This leading reference, now in its third edition, deals with the
statistical methods for analyzing survival data derived from
laboratory studies of animals, clinical and epidemiological
studies of humans, and other appropriate applications. Special
consideration is given to the study of survival data in
biomedical sciences, though all the methods are suitable for
applications in industrial reliability, social sciences, and
business.
ISBN: 0-471-37216-1
Hardcover
848 Pages
February 2003
Table of Contents
PART I: FUNDAMENTAL STATISTICS CONCEPTS.
Statistics in Engineering and Science.
Fundamentals of Statistical Inference.
Inferences on Means and Standard Deviations.
PART II: DESIGN AND ANALYSIS WITH FACTORIAL STRUCTURE.
Statistical Principles in Experimental Design.
Factorial Experiments in Completely Randomized Designs.
Analysis of Completely Randomized Designs.
Fractional Factorial Experiments.
Analysis of Fractional Factorial Experiments.
PART III: DESIGN AND ANALYSIS WITH RANDOM FACTOR EFFECTS.
Experiments in Randomized Block Designs.
Analysis of Designs with Random Factor Levels.
Nested Designs.
Special Designs for Process Improvement.
Analysis of Nested Designs and Designs for Process Improvement.
PART IV: DESIGN AND ANALYSIS WITH QUANTITATIVE PREDICTORS AND
FACTORS.
Linear Regression with One Predicator Variables.
Linear Regression with Several Predicator Variable.
Linear Regression with Factors and Covariates as Predictors.
Designs and Analyses for Fitting Response Surfaces.
Model Assessment.
Variable Selection Techniques.
Appendix: Statistical Tables.
Index.
Index of Data Sets.
This practitioners guide to statistical methods for
designing and analyzing experiments has been a highly successful
resource for engineers and scientists who utilize statistical
approaches to solving problems in an experimental setting. Now in
its Second Edition, the book gathers together the statistical
techniques most useful to experimenters and data analysts who
must either collect, analyze, or interpret data. The material is
of value to managers, supervisors, and other administrators who
must make decisions based in part on the analyses of data that
may have been performed by others.
ISBN: 0-471-33052-3
Hardcover
640 Pages
March 2003
Table of Contents
Preface.
Frequently Used Notation.
Stochastic Search and Optimization: Motivation and Supporting
Results.
Direct Methods for Stochastic Search.
Recursive Estimation for Linear Models.
Stochastic Approximation for Nonlinear Root-Finding.
Stochastic Gradient Form of Stochastic Approximation.
Stochastic Approximation and the Finite-Difference Method.
Simultaneous Perturbation Stochastic Approximation.
Annealing-Type Algorithms.
Evolutionary Computation I: Genetic Algorithms.
Evolutionary Computation II: General Methods and Theory.
Reinforcement Learning via Temporal Differences.
Statistical Methods for Optimization in Discrete Problems.
Model Selection and Statistical Information.
Simulation-Based Optimization I: Regeneration, Common Random
Numbers, and Selection Methods.
Simulation-Based Optimization II: Stochastic Gradient and Sample
Path Methods.
Markov Chain Monte Carlo.
Optimal Design for Experimental Inputs.
Appendix A. Selected Results from Multivariate Analysis.
Appendix B. Some Basic Tests in Statistics.
Appendix C. Probability Theory and Convergence.
Appendix D. Random Number Generation.
Appendix E. Markov Processes.
Answers to Selected Exercises.
References.
Index.
A strongly interdisciplinary book with potential and actual
applications of the material in branches of mathematics,
engineering, science, and social sciences, this reference covers
a broad range of the most popular stochastic algorithms,
including random search, experimental design methods, stochastic
approximation, simulated annealing, genetic and evolutionary
methods, and machine learning.