Parameter estimation by minimizing a probability generating function-based power divergence
Generating function-based statistical inference is an attractive approach if the probability (density) function is complicated when compared with the generating function. Here, we propose a parameter estimation method that minimizes a probability generating function (pgf)-based power divergence with...
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| Format: | Article |
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Taylor & Francis
2019
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| author | Tay, Siew Ying Ng, Choung Min Ong, Seng Huat |
| author_facet | Tay, Siew Ying Ng, Choung Min Ong, Seng Huat |
| author_sort | Tay, Siew Ying |
| collection | UM |
| description | Generating function-based statistical inference is an attractive approach if the probability (density) function is complicated when compared with the generating function. Here, we propose 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. The proposed estimator is linked to the M-estimators and hence possesses the properties of consistency and asymptotic normality. In terms of parameter biases and mean squared errors from simulations, the proposed estimation method performs better for smaller value of the tuning parameter as data contamination percentage increases. © 2018, © 2018 Taylor & Francis Group, LLC. |
| first_indexed | 2024-03-06T05:59:23Z |
| format | Article |
| id | um.eprints-23270 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:59:23Z |
| publishDate | 2019 |
| publisher | Taylor & Francis |
| record_format | dspace |
| spelling | um.eprints-232702019-12-23T01:52:50Z http://eprints.um.edu.my/23270/ Parameter estimation by minimizing a probability generating function-based power divergence Tay, Siew Ying Ng, Choung Min Ong, Seng Huat Q Science (General) QA Mathematics Generating function-based statistical inference is an attractive approach if the probability (density) function is complicated when compared with the generating function. Here, we propose 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. The proposed estimator is linked to the M-estimators and hence possesses the properties of consistency and asymptotic normality. In terms of parameter biases and mean squared errors from simulations, the proposed estimation method performs better for smaller value of the tuning parameter as data contamination percentage increases. © 2018, © 2018 Taylor & Francis Group, LLC. Taylor & Francis 2019 Article PeerReviewed Tay, Siew Ying and Ng, Choung Min and Ong, Seng Huat (2019) Parameter estimation by minimizing a probability generating function-based power divergence. Communications in Statistics - Simulation and Computation, 48 (10). pp. 2898-2912. ISSN 0361-0918, DOI https://doi.org/10.1080/03610918.2018.1468462 <https://doi.org/10.1080/03610918.2018.1468462>. https://doi.org/10.1080/03610918.2018.1468462 doi:10.1080/03610918.2018.1468462 |
| spellingShingle | Q Science (General) QA Mathematics Tay, Siew Ying Ng, Choung Min Ong, Seng Huat Parameter estimation by minimizing a probability generating function-based power divergence |
| title | Parameter estimation by minimizing a probability generating function-based power divergence |
| title_full | Parameter estimation by minimizing a probability generating function-based power divergence |
| title_fullStr | Parameter estimation by minimizing a probability generating function-based power divergence |
| title_full_unstemmed | Parameter estimation by minimizing a probability generating function-based power divergence |
| title_short | Parameter estimation by minimizing a probability generating function-based power divergence |
| title_sort | parameter estimation by minimizing a probability generating function based power divergence |
| topic | Q Science (General) QA Mathematics |
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