In estimating the parameters of the two-parameter Pareto distribution it is well known that the performance of the maximum likelihood estimator deteriorates when sample sizes are small or the underlying model is contaminated. In this paper we propose a new parameter estimator that utilizes a pivotal quantity based on the regression framework, allowing separate estimation of the two parameters in a straightforward manner. The consistency of the estimator is also established. Simulation studies show that the proposed estimator is a competitive, well-rounded robust estimator for both Pareto and contaminated Pareto datasets when the sample sizes are small.
Bibliographical noteFunding Information:
The research of first author is supported by Basic Science Research Program of the National Research Foundation of Korea ( NRF-2015R1A1A1A05027336 ).
The work of corresponding author was supported by the 2016 Research Fund of the University of Seoul .
© 2017 The Korean Statistical Society
All Science Journal Classification (ASJC) codes
- Statistics and Probability