On Minimum Distance Estimation in Dose Response Studies

  • Author / Creator
    Zhao, Bangxin
  • In this thesis, two robust and efficient methods of estimation are examined in dose-response studies context. In particular, we investigate the minimum Hellinger distance estimation and symmetric chi-squared distance methods of estimation. We support our theoretical results with extensive finite sample simulation studies. Based on the minimum Hellinger distance and symmetric chi-squared distance approaches, new estimators of the regression parameters are derived for logistic and probit models. Then their asymptotic properties such as consistency and asymptotic normality are investigated. It is shown that our minimum Hellinger distance estimator is asymptotically equivalent to the traditional estimators derived using the maximum likelihood and weighted least squares approaches. Simulation studies are used to demonstrate that the new estimators work as good as the traditional estimators and most often outperforms them when a contamination occurs in the data. Lastly, the proposed methods are used to estimate the critical dose.

  • Subjects / Keywords
  • Graduation date
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
  • Institution
    University of Alberta
  • Degree level
  • Department
    • Department of Mathematical and Statistical Sciences
  • Specialization
    • Statistics
  • Supervisor / co-supervisor and their department(s)
    • Dr. Rohana Karunamuni (Department of Mathematical and Statistical Scienses)
  • Examining committee members and their departments
    • Dr. Yan Yuan (School of Public Health Sciences)
    • Dr. Rohana Karunamuni (Department of Mathematical and Statistical Scienses)
    • Dr. Linglong Kong (Department of Mathematical and Statistical Scienses)