Parameter estimation using generating function based minimum power divergence measure / Tay Siew Ying
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 t...
| Main Author: | |
|---|---|
| Format: | Thesis |
| Published: |
2018
|
| Subjects: | |
| Online Access: | http://studentsrepo.um.edu.my/9535/ http://studentsrepo.um.edu.my/9535/1/Tay_Siew_Ying.pdf http://studentsrepo.um.edu.my/9535/9/siew_ying.pdf |
| Summary: | 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 |
|---|