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: Nonparametric estimation of econometric models with categorical variables
  name:
    name part: Ouyang, Desheng
    name part: 1967-
    role:
      role term: creator
  name:
    name part: Li, Qi
    role:
      role term: thesis advisor.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 2006
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: remote
  abstract: In this dissertation I investigate several topics in the field of nonparametric econometrics. In chapter II, we consider the problem of estimating a nonparametric regression model with only categorical regressors. We investigate the theoretical propertiesof least squares cross-validated smoothing parameter selection, establish the rate of convergence (to zero) of the smoothing parameters for relevant regressors, and show that there is a high probability that the smoothing parameters for irrelevant regressors converge to their upper bound values thereby smoothing out the irrelevant regressors. In chapter III, we consider the problem of estimating a joint distribution defined over a set of discrete variables. We use a smoothing kernel estimator to estimate the joint distribution, allowing for the case in which some of the discrete variables are uniformly distributed, and explicitly address the vector-valued smoothing parameter case due to its practical relevance. We show that the cross-validated smoothing parameters differ in their asymptotic behavior depending on whether a variable is uniformly distributed or not. In chapter IV, we consider a k-n-n estimation of regression function with k selected by a cross validation method. We consider both the local constant and local linear cases. In both cases, the convergence rate of of the cross validated k is established. In chapter V, we consider nonparametric estimation of regression functions with mixed categorical and continuous data. The smoothing parameters in the model are selected by a cross-validation method. The uniform convergence rate of the kernel regression function estimator function with weakly dependent data is derived.
  note: by Desheng Ouyang.
  note: "Major Subject: Economics"
  note: Title from author supplied metadata (automated record created on Feb. 23, 2007.)
  note: Vita.
  note: Abstract.
  note: Electronic resource.
  note: Mode of access: World Wide Web.
  note: System requirements: World Wide Web access and Adobe Acrobat Reader.
  note: Ph. D. Texas A&M University 2005.
  note: Includes bibliographical references.
  note: Text (Dissertation).
  subject:
    topic: Major economics
  subject:
    topic: CATEGORICAL
  subject:
    topic: NONPARAQMETRIC
  identifier: http://hdl.handle.net/1969.1/4298
  record info:
    record content source: TXA
    record creation date: 070322
    record change date: 20151104162918.0
    record identifier: in00002222221