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Activity Number: 531 - SPEED: Statistical Computing: Methods, Implementation, and Application, Part 2
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 11:35 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract #307949
Title: A New Approach in Distribution Fitting for Grouped Data and Its Application in Measuring Income Distribution
Author(s): Ying-Ju Chen* and Tatjana Miljkovic
Companies: University of Dayton and Miami University
Keywords: de-grouping data; acceptance-rejection sampling; insurance losses

A new method is proposed for data de-grouping when the frequency and average value of observations are provided for each interval in the grouped data. An extensive simulation study is completed to evaluate the performance of the proposed method on curve fittings using different parametric models. Our method is found to be superior to the uniform method previously used in data de-grouping. The results of the simulation study are promising and they show that this method can be used successfully in the applications where the data de-grouping is desired. To illustrate the performance of the proposed method, we fit univariate distributions to insurance losses reported for bodily injury liability data. Additionally, we fit the finite mixture models with the EM algorithm using Gamma, Lognormal, and Weibull distributions through the same data. In addition, the approach is used to correct bias in Gini coefficient due to data grouping methods.

Authors who are presenting talks have a * after their name.

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