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: Multivariate receptor modeling from a statistical science viewpoint
  name:
    name part: Park, Eun Sug
    name part: 1967-
    role:
      role term: creator
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: xx
    date issued: 1997
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: print
    extent: xi, 153 leaves : illustrations ; 28 cm.
  abstract: Receptor modeling is a collection of methods used to model
  abstract: the air pollution data. Although the main interest is
  abstract: identifying pollution sources, the determination of the
  abstract: number of major sources is the first problem that we
  abstract: should overcome. The NUMFACT algorithm is a new method of
  abstract: determining the number of underlying factors in a
  abstract: multivariate system. Although NUMFACT has not been
  abstract: previously described, it has been used in high profile
  abstract: studies. The asymptotic distribution of NUMFACT
  abstract: statistics and their associated cutoff points are derived.
  abstract: Modified NUMFACT statistics and related statistics, W, are
  abstract: also presented with a new decision rule for determining
  abstract: the number of sources. The simulation study shows that
  abstract: for the lognormal error case, they are superior to the
  abstract: traditional methods such as Bartlett's statistic or the
  abstract: rule of thumb methods, and for the normal error case, they
  abstract: are competitive with the best of the traditional methods.
  abstract: Many air pollution datasets typically consist of the
  abstract: measurements on fifty or sixty variables, and it is often
  abstract: too large to handle all at once. We develop two new
  abstract: algorithms, SPECIESA and SPECIESB, for choosing the
  abstract: species used in final model fitting. The simulation
  abstract: results show that these algorithms work very well in
  abstract: choosing the species generated by the sources not by the
  abstract: measurement errors. Estimating the source profiles and
  abstract: their contributions are our primary concerns in receptor
  abstract: modeling. We take the constrained nonlinear least squares
  abstract: approach to provide those estimates under as little
  abstract: assumptions as possible about the model parameters. The
  abstract: assumptions that we make are necessary to get the model
  abstract: identifiability. A set of algorithms, VERTEX, to find the
  abstract: least squares solution under different versions of the
  abstract: model is introduced. The resulting estimators are shown
  abstract: to be consistent and asymptotically normal under
  abstract: appropriate identifiability conditions. The proposed
  abstract: methods are applied to simulated data and real air
  abstract: pollution data.
  note: by Eun Sug Park.
  note: Vita.
  note: "Major Subject: Statistics".
  note: Issued also on microfiche from University Microfilms Inc.
  note: Includes bibliographical references: pages 143-147.
  note: Ph. D. Texas A&M University 1997.
  subject:
    topic: Major statistics
  identifier: 98-15820 UMI
  identifier: http://proxy.library.tamu.edu/login
  related item:
    location:
      url: http://proxy.library.tamu.edu/login
  record info:
    record content source: TXA
    record creation date: 980708
    record change date: 20190325093517.0
    record identifier: in00001422321