Maximum Likelihood Estimation of Logistic Sinusoidal Regression Models Metadata

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Title

  • Main Title Maximum Likelihood Estimation of Logistic Sinusoidal Regression Models

Creator

  • Author: Weng, Yu
    Creator Type: Personal

Contributor

  • Chair: Song, Kai-Sheng
    Contributor Type: Personal
    Contributor Info: Major Professor
  • Chair: Allaart, Pieter C.
    Contributor Type: Personal
  • Chair: Wang, Jiaping
    Contributor Type: Personal

Publisher

  • Name: University of North Texas
    Place of Publication: Denton, Texas
    Additional Info: www.unt.edu

Date

  • Creation: 2013-12

Language

  • English

Description

  • Content Description: We consider the problem of maximum likelihood estimation of logistic sinusoidal regression models and develop some asymptotic theory including the consistency and joint rates of convergence for the maximum likelihood estimators. The key techniques build upon a synthesis of the results of Walker and Song and Li for the widely studied sinusoidal regression model and on making a connection to a result of Radchenko. Monte Carlo simulations are also presented to demonstrate the finite-sample performance of the estimators

Subject

  • Keyword: Maximum likelihood
  • Keyword: estimation
  • Keyword: logistic regression
  • Keyword: sinusoidal regression

Collection

  • Name: UNT Theses and Dissertations
    Code: UNTETD

Institution

  • Name: UNT Libraries
    Code: UNT

Rights

  • Rights Access: public
  • Rights Holder: Weng, Yu
  • Rights License: copyright
  • Rights Statement: Copyright is held by the author, unless otherwise noted. All rights Reserved.

Resource Type

  • Thesis or Dissertation

Format

  • Text

Identifier

  • Archival Resource Key: ark:/67531/metadc407796

Degree

  • Academic Department: Department of Mathematics
  • Degree Discipline: Mathematics
  • Degree Level: Doctoral
  • Degree Name: Doctor of Philosophy
  • Degree Grantor: University of North Texas
  • Degree Publication Type: disse

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