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A quantile estimation for massive data with generalized Pareto distribution

Title
A quantile estimation for massive data with generalized Pareto distribution
Authors
Song J.Song S.
Ewha Authors
송종우
SCOPUS Author ID
송종우scopus
Issue Date
2012
Journal Title
Computational Statistics and Data Analysis
ISSN
0167-9473JCR Link
Citation
vol. 56, no. 1, pp. 143 - 150
Indexed
SCIE; SCOPUS WOS scopus
Abstract
This paper proposes a new method of estimating extreme quantiles of heavy-tailed distributions for massive data. The method utilizes the Peak Over Threshold (POT) method with generalized Pareto distribution (GPD) that is commonly used to estimate extreme quantiles and the parameter estimation of GPD using the empirical distribution function (EDF) and nonlinear least squares (NLS). We first estimate the parameters of GPD using EDF and NLS and then, estimate multiple high quantiles for massive data based on observations over a certain threshold value using the conventional POT. The simulation results demonstrate that our parameter estimation method has a smaller Mean square error (MSE) than other common methods when the shape parameter of GPD is at least 0. The estimated quantiles also show the best performance in terms of root MSE (RMSE) and absolute relative bias (ARB) for heavy-tailed distributions. © 2011 Elsevier B.V. All rights reserved.
DOI
10.1016/j.csda.2011.06.030
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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