Together with China Science Publishing & Media Ltd.
Starting with an introduction to fractional derivatives and numerical
approximations, this book presents finite difference methods for fractional
differential equations, including time-fractional sub-diffusion equations,
time-fractional wave equations, and space-fractional differential equations,
among others. Approximation methods for fractional derivatives are
developed and approximate accuracies are analyzed in detail.
} A unique overview of finite difference methods for fractional differential equations.
} Supplied with numerous examples to facilitate understanding.
} Of interest to applied mathematicians and physicists as well as to engineers.
Zhi-zhong Sun, Southeast University, Nanjing, China; Guanghua Gao,
Nanjing University of Posts and Telecommunications, Nanjing, China.
XVI, 380 pages, 0 figures (bw),15 tables (bw)
Hardcover:
ISBN 978-3-11-061517-3
Date of publication: August 2020
Language of publication: English
Subjects: Mathematics } Differential Equations and Dynamical Systems Mathematics } Analysis
Of interest to: Researchers and graduate students in mathematics, physics, and engineering
A publication of Hindustan Book Agency
This book stems from lectures that were given at a three-week Advanced Instructional School on Ergodic Theory and Dynamical Systems held at the Indian Institute of Technology, Delhi during December 2017, organized by the National Centre for Mathematics (NCM) with support from the National Board for Higher Mathematics (NBHM), Department of Atomic Energy (DAE), Government of India.
These lecture notes are intended to help a new researcher understand various aspects of dynamical systems. Each chapter of this book specializes in one aspect of dynamical systems; and, thus, begins at an elementary level and goes on to cover fairly advanced material. The editors hope the book will help researchers get familiar with, and navigate through, different parts of ergodic theory and dynamical systems.
Researchers interested in various aspects of dynamical systems.
Hindustan Book Agency Volume: 79
2020; 214 pp; Hardcover
MSC: Primary 37;
Print ISBN: 978-93-86279-83-5
ISBN: 978-1-119-24380-9 November 2020 736 Pages
Hardcover
Updated classic statistics text, with new problems and examples
Probability and Statistical Inference, Third Edition helps students grasp essential concepts of statistics and its probabilistic foundations. This book focuses on the development of intuition and understanding in the subject through a wealth of examples illustrating concepts, theorems, and methods. The reader will recognize and fully understand the why and not just the how behind the introduced material.
In this Third Edition, the reader will find a new chapter on Bayesian statistics, 70 new problems and an appendix with the supporting R code. This book is suitable for upper-level undergraduates or first-year graduate students studying statistics or related disciplines, such as mathematics or engineering. This Third Edition:
Introduces an all-new chapter on Bayesian statistics and offers thorough explanations of advanced statistics and probability topics
Includes 650 problems and over 400 examples - an excellent resource for the mathematical statistics class sequence in the increasingly popular "flipped classroom" format
Offers students in statistics, mathematics, engineering and related fields a user-friendly resource
Provides practicing professionals valuable insight into statistical tools
Probability and Statistical Inference offers a unique approach to problems that allows the reader to fully integrate the knowledge gained from the text, thus, enhancing a more complete and honest understanding of the topic.
PERMISSIONS
ISBN: 978-1-119-47010-6 December 2020 784 Pages
Hardcover
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 an integrated system of experimental design and analysis that can be applied across various fields of research including engineering, medicine, and the physical sciences. New chapters provide modern updates on practical optimal design and computer experiments, an explanation of computer simulations as an alternative to physical experiments. Each chapter begins with a real-world example of an experiment followed by the methods required to design that type of experiment. The chapters conclude with an application of the methods to the experiment, bridging the gap between theory and practice.
The authors modernize accepted methodologies while refining many cutting-edge topics including robust parameter design, analysis of non-normal data, analysis of experiments with complex aliasing, multilevel designs, minimum aberration designs, and orthogonal arrays.
The third edition includes:
Information on the design and analysis of computer experiments
A discussion of practical optimal design of experiments
An introduction to conditional main effect (CME) analysis and definitive screening designs (DSDs)
New exercise problems
This book includes valuable exercises and problems, allowing the reader to gauge their progress and retention of the book's subject matter as they complete each chapter.
Drawing on examples from their combined years of working with industrial clients, the authors present many cutting-edge topics in a single, easily accessible source. Extensive case studies, including goals, data, and experimental designs, are also included, and the book's data sets can be found on a related FTP site, along with additional supplemental material. Chapter summaries provide a succinct outline of discussed methods, and extensive appendices direct readers to resources for further study.
Experiments: Planning, Analysis, and Optimization, Third Edition is an excellent book for design of experiments courses at the upper-undergraduate and graduate levels. It is also a valuable resource for practicing engineers and statisticians.
C. F. JEFF WU, PHD, is Coca-Cola Professor in Engineering Statistics at the Georgia Institute of Technology. Dr. Wu has published more than 180 papers and is the recipient of numerous accolades, including the National Academy of Engineering membership and the COPSS Presidents' Award.
MICHAEL S. HAMADA, PHD, is Senior Scientist at Los Alamos National Laboratory (LANL) in New Mexico. Dr. Hamada is a Fellow of the American Statistical Association, a LANL Fellow, and has published more than 160 papers.
ISBN: 978-1-119-37352-0 October 2020 848 Pages
Hardcover
Handbook and reference for industrial statisticians and system reliability engineers
System Reliability Theory: Models, Statistical Methods, and Applications, Third Edition presents an updated and revised look at system reliability theory, modeling, and analytical methods. The new edition is based on feedback to the second edition from numerous students, professors, researchers, and industries around the world. New sections and chapters are added together with new real-world industry examples, and standards and problems are revised and updated.
System Reliability Theory covers a broad and deep array of system reliability topics, including:
・ In depth discussion of failures and failure modes
・ The main system reliability assessment methods
・ Common-cause failure modeling
・ Deterioration modeling
・ Maintenance modeling and assessment using Python code
・ Bayesian probability and methods
・ Life data analysis using R
Perfect for undergraduate and graduate students taking courses in reliability engineering, this book also serves as a reference and resource for practicing statisticians and engineers.
Throughout, the book has a practical focus, incorporating industry feedback and real-world industry problems and examples.
MARVIN RAUSAND is Professor Emeritus in the department of Mechanical and Industrial Engineering at the Norwegian University of Science and Technology (NTNU), Norway, and author of Risk Assessment: Theory, Methods, and Applications and Reliability of Safety-Critical Systems: Theory and Applications, both published by Wiley.
ANNE BARROS, PHD, is Professor in reliability and maintenance engineering at Ecole CentraleSupelec, University of Paris-Saclay, France. Her research focus is on degradation modeling, prognostics, condition based and predictive maintenance. She got a PHD then a professorship position at University of Technology of Troyes, France (2003 ? 2014) and spent five years as a full-time professor at NTNU, Norway (2014 ? 2019). She is currently heading a research group and holds an industrial chair at CentraleSupelec with the ambition to provide reliability assessment and maintenance modeling methods for systems of systems.
The late ARNLJOT HOYLAND, PHD, was a Professor in the Department of Mathematical Sciences at the Norwegian University of Science and Technology.
ISBN: 978-1-119-57872-7 January 2021 784 Pages
Hardcover
This book presents both the conventional and less common uses of linear regression in today’s cutting-edge scientific research. The authors blend both theory and application to equip readers with an understanding of the basic principles needed to apply regression model-building techniques in various fields of study, including engineering, management, and the health sciences. The authors focused on four areas of improvement for this new edition: new exercises and data sets, new material on generalized regression techniques, the inclusion of JMP software in key places, and finally, the authors focused on carefully condensing the text where possible. This book begins with an introduction of regression and model building. This is followed by chapters on simple linear regression, multiple linear regression, model adequacy checking, transformations and weighting to correct model inadequacies, and diagnostics for leverage and influence. The book also covers polynomial and nonparametric regression models, indicator variables, and multicollinearity.
SERIES : Wiley Series in Probability and Statistics
ISBN: 978-1-119-41738-5 January 2021
Hardcover
This book presents methods useful for analyzing and understanding large data sets that are dynamically dependent. The book will begin with examples of multivariate dependent data and tools for presenting descriptive statistics of such data. It then introduces some useful statistical methods for univariate time series analysis emphasizing on statistical procedures for modeling and forecasting. Both linear and nonlinear models are discussed. Special attention is given to analysis of high-frequency dependent data.
The second part of the book considers joint dependency, both contemporaneous and dynamical dependence, among multiple series of dependent data. Special attention will be given to graphical methods for large data, to handling heterogeneity in time series (such as outliers, missing values, and changes in the covariance matrices), and to time-varying parameters for multivariate time series. The third part of the book is devoted to analysis of high-dimensional dependent data. The focus is on topics that are useful when the number of time series is large. The selected topics include clustering time series, high-dimensional linear regression for dependent data and its applications, and reducing the dimension with dynamic principal components and factor models. Throughout the book, advantages and disadvantages of the methods discussed are given and real examples are used in demonstration.
The book will be of interest to graduate students, researchers, and practitioners in business, economics, engineering, and science who are interested in statistical methods for analyzing big dependent data and forecasting.