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

  • Minimum Distance Estimation
  • Robust Statistics
  • Dose Response Studies

• Graduation date

  Fall 2013

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R37H1DV93

• 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.