rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Process Optimization
    sub title: A Statistical Approach
  name:
    name part: Castillo, Enrique Del.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2007
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVIII, 462 p. 76 illus. online resource.
  abstract: PROCESS OPTIMIZATION: A Statistical Approach is a textbook for a course in experimental optimization techniques for industrial production processes and other "noisy" systems where the main emphasis is process optimization. The book can also be used as a reference text by Industrial, Quality and Process Engineers and Applied Statisticians working in industry, in particular, in semiconductor/electronics manufacturing and in biotech manufacturing industries. The major features of PROCESS OPTIMIZATION: A Statistical Approach are: It provides a complete exposition of mainstream experimental design techniques, including designs for first and second order models, response surface and optimal designs; Discusses mainstream response surface method in detail, including unconstrained and constrained (i.e., ridge analysis and dual and multiple response) approaches; Includes an extensive discussion of Robust Parameter Design (RPD) problems, including experimental design issues such as Split Plot designs and recent optimization approaches used for RPD; Presents a detailed treatment of Bayesian Optimization approaches based on experimental data (including an introduction to Bayesian inference), including single and multiple response optimization and model robust optimization; Provides an in-depth presentation of the statistical issues that arise in optimization problems, including confidence regions on the optimal settings of a process, stopping rules in experimental optimization and more; Contains a discussion on robust optimization methods as used in mathematical programming and their application in response surface optimization; Offers software programs written in MATLAB and MAPLE to implement Bayesian and frequentist process optimization methods; Provides an introduction to the optimization of computer and simulation experiments including and introduction to stochastic approximation and stochastic perturbation stochastic approximation (SPSA) methods; Includes an introduction to…
  table of contents: Preliminaries -- An Overview of Empirical Process Optimization -- Elements of Response Surface Methods -- Optimization Of First Order Models -- Experimental Designs For First Order Models -- Analysis and Optimization of Second Order Models -- Experimental Designs for Second Order Models -- Statistical Inference in Process Optimization -- Statistical Inference in First Order RSM Optimization -- Statistical Inference in Second Order RSM Optimization -- Bias Vs. Variance -- Robust Parameter Design and Robust Optimization -- Robust Parameter Design -- Robust Optimization -- Bayesian Approaches in Process Optimization -- to Bayesian Inference -- Bayesian Methods for Process Optimization -- to Optimization of Simulation and Computer Models -- Simulation Optimization -- Kriging and Computer Experiments -- Appendices -- Basics of Linear Regression -- Analysis of Variance -- Matrix Algebra and Optimization Results -- Some Probability Results Used in Bayesian Inference.
  note: by Enrique Del Castillo.
  subject:
    topic: Mathematics
  subject:
    topic: Mathematical models
  subject:
    topic: Probabilities
  subject:
    topic: Statistics
  subject:
    topic: Engineering design
  subject:
    topic: Quality control
  subject:
    topic: Reliability
  subject:
    topic: Industrial safety
  subject:
    topic: Mathematics
  subject:
    topic: Probability Theory and Stochastic Processes
  subject:
    topic: Quality Control, Reliability, Safety and Risk
  subject:
    topic: Engineering Design
  subject:
    topic: Statistical Theory and Methods
  subject:
    topic: Statistics, general
  subject:
    topic: Mathematical Modeling and Industrial Mathematics
  classification: QA273.A1-274.9
  classification: QA274-274.9
  classification: 519.2
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: International Series in Operations Research & Management Science, 105
  identifier: 9780387714356
  identifier: http://dx.doi.org/10.1007/978-0-387-71435-6
  record info:
    record creation date: 100301
    record change date: 20151204184006.0
    record identifier: 978-0-387-71435-6