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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| Main Authors: | , , |
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| Format: | Article |
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Taylor & Francis
2019
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/23270/ https://doi.org/10.1080/03610918.2018.1468462 |
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| Summary: | 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. |
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