Parameter estimation by minimizing a probability generating function-based power divergence
Document Type
Article
Publication Date
1-1-2019
Abstract
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.
Keywords
Density power divergence, Hellinger distance, Jeffreys’ divergence, M-estimation, Probability generating function
Divisions
MathematicalSciences
Funders
Ministry of Higher Education, Malaysia under the FRGS grants FP014-2012A and FP045-2015A
Publication Title
Communications in Statistics - Simulation and Computation
Volume
48
Issue
10
Publisher
Taylor & Francis