Hoang Pham (editor) - Springer Handbook of Engineering Statistics

Managing Editor

Editor

2nd ed. 2023

With 400 Figures and 203 Tables

Editor

ISSN 2522-8692 e-ISSN 2522-8706

ISBN 978-1-4471-7502-5 e-ISBN 978-1-4471-7503-2

https://doi.org/10.1007/978-1-4471-7503-2

Springer-Verlag London Ltd., part of Springer Nature 2006, 2023

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Preface

In response to such technological trends along with much feedback that we have received from users of the first edition of the Springer Handbook of Engineering Statistics (published in 2006), we have made substantial changes in this new edition by The Springer Handbook of Engineering Statistics, altogether 56 chapters, provides a comprehensive state-of-the-art reference volume that covers both fundamental and theoretical work in the areas of engineering statistics including: adaptive progressive censoring, AI, accelerated life testing, adversarial learning, artificial neural network, Bayesian inferences and prediction, Bootstrap and boosting methods, censored survival data, competing risk models, change-point detection, correlated failures, counting processes, copula methods, data mining, deep learning, dynamic robust design, difference of convex functions (DC), detecting outliers in high-dimensional data, dimension reduction and nonparametric methods, distributional inference, electronic packaging, fatigue life prediction, functional dependence measure, fusion learning, genetic algorithms, exponentially weighted moving average, gradient method, high-dimension time series, importance measures, indecisive weighted voting system, logistic regression, machine learning, multi-agent support systems, majorization-minimization, Monte Carlo simulation, influential observations, Markov decision process, model selection, multivariate distributions, multiple stresses designs, multivariate time series, multi-criteria decisions, nonparametric empirical Bayesian, inspection-maintenance, order statistics, product reliability, repairable system reliability, renewal processes, process control and improvement, proportional hazards regression and sampling, random forests for censoring data, recursive partitioning algorithm, software reliability, semi-parametric for survival data, step-stress life testing, statistical process control, six sigma design and methodology, stationary causal process, stochastic dependences, statistical design and diagnostics, stochastic expectation maximization algorithm, stress-strength models, unsupervised learning, variable importance in high-dimensional, voltage, and vibration acceleration models, among others.

The contents of the handbook are organized in six parts: The chapters are written by more than 110 leading experts in statistics, biostatistics, engineering statistics, reliability engineering, computer science, applied statistics, machine learning, management, and related areas.