Parameter estimation by minimizing a probability generating function-based power divergence

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

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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.

Item Type: Article
Funders: Ministry of Higher Education, Malaysia under the FRGS grants FP014-2012A and FP045-2015A
Uncontrolled Keywords: Density power divergence; Hellinger distance; Jeffreys’ divergence; M-estimation; Probability generating function
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics
Divisions: Faculty of Science > Institute of Mathematical Sciences
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 23 Dec 2019 01:52
Last Modified: 23 Dec 2019 01:52

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