Parameter estimation using generating function based minimum power divergence measure / Tay Siew Ying

Tay, Siew Ying (2018) Parameter estimation using generating function based minimum power divergence measure / Tay Siew Ying. Masters thesis, University of Malaya.

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      Abstract

      This research proposes a parameter estimation method that minimizes a probability generating function (pgf) based power divergence with a tuning parameter to mitigate the impact of data contamination. Special cases arise when the tuning parameter approaches zero, resulting in a Kullback-Leibler type divergence, and when it takes on the value of one, resulting in a pgf-based

      Item Type: Thesis (Masters)
      Additional Information: Dissertation (M.A.) – Faculty of Science, University of Malaya, 2018.
      Uncontrolled Keywords: Asymptotic normality; Density power divergence; M-estimators; Probability generating function; Robustness
      Subjects: Q Science > Q Science (General)
      Divisions: Faculty of Science
      Depositing User: Mr Mohd Safri Tahir
      Date Deposited: 08 Feb 2019 07:24
      Last Modified: 20 Feb 2019 08:07
      URI: http://studentsrepo.um.edu.my/id/eprint/9535

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