DATE2022-06-02 15:00-16:00

PLACE數學系館 1F3174教室

SPEAKER陳映如(Ying-Ju Tessa Chen) 助理教授(Department of Mathematics University of Dayton

TITLEA New Computational Approach for Bias Reduction of The Gini Index Estimation Based on Grouped Data

Many government agencies still rely on the grouped data as the main source of information for calculation of the Gini index. Previous research showed that the Gini index based on the grouped data suffers the first and second-order correction bias compared to the Gini index computed based on the individual data. Since the accuracy of the estimated correction bias is subject to many underlying assumptions, we propose a new method, D-Gini, which reduces the bias in Gini coefficient based on grouped data. We investigate the performance of the D-Gini method on an open-ended tail interval of the income distribution. The results of the simulation study showed that our method is very effective in minimizing the first and second order-bias in the Gini index and outperforms other methods previously used for the bias-correction of the Gini index based on grouped data. Three data sets are used to illustrate the application of this method. I will talk about some ongoing work based on this study as well.
會議號:2517 335 6389