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:
    non sort: An
    title: algorithm for the optimization of response surface designs
  name:
    name part: Ruud, Paul Gordon.
    role:
      role term: creator
  name:
    name part: Freund, R. J.
    role:
      role term: degree committee member.
  name:
    name part: Gates, Charles E.
    role:
      role term: degree supervisor.
  name:
    name part: Harley, H. O.
    role:
      role term: degree committee member.
  name:
    name part: Moore, Bill C.
    role:
      role term: degree committee member.
  name:
    name part: Texas A & M University
    role:
      role term: degree granting institution.
  type of resource: text
  genre: theses
  genre: Academic theses
  origin info:
    place:
      place term: txu
    date issued: 1969
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: print
    extent: 70 leaves
  abstract: An algorithm is developed for the optimization of second order response surface designs. The technique of response surface analysis consists of two stages: (a) the design of the experimental plan consisting of N individual trials and (b) using the data from (a) to estimate the coefficients in a mathematical formula representing the relationship between inputs and responses. This research is concerned with stage (a) adopting the approach to construct an optimal design for an experiment rather than tailoring the experiment so as to fit a known design. The response surfaces considered are quadratic in nature; that is, each of the n 'input' variables appears in the model as zero, first and second order variables. Each of these variables is limited to some region of interest for any given experiment. The criterion adopted here is the minimization of the 'generalized variance' of the estimated coefficients. Thus the statistical problem in summary is to determine the levels of the n 'input' variables for each of the N trials so as to make the generalized variance as small as possible. This translates to the mathematical programming problem of maximizing an objective function which is the reciprocal of the generalized variance subject to restraining the variables in the experimental region of Interest. This mathematical programming problem is solved using the technique of Spherical Programming which is capable of finding the global maximum of a concave function subject to linear constraints. This is essentially a steepest ascent algorithm which creates and solves a sequence of subproblems leading to the solution of the main problem. The objective function for this problem, however, is not concave; none the less, this technique may be used to solve the problem at the risk of the algorithm terminating at a relative optimum rather than a global optimum. A stochastic control procedure is introduced which allows comparison of the algorithm result with an estimated global optimum…
  note: Ph. D. in Statistics Texas A & M University
  subject:
    topic: Major statistics
  identifier: http://proxy.library.tamu.edu/login
  identifier: https://hdl.handle.net/1969.1/DISSERTATIONS-175476
  related item:
    location:
      url: http://proxy.library.tamu.edu/login
  related item:
    location:
      url: https://hdl.handle.net/1969.1/DISSERTATIONS-175476
  record info:
    record content source: TXA
    record creation date: 791121
    record change date: 20220103135208.0
    record identifier: in00000153336
    language of cataloging:
      language term: eng