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: | 14 Jul 2021 03:13 |
| URI: | http://studentsrepo.um.edu.my/id/eprint/9535 |
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