Abstract #301112

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JSM 2003 Abstract #301112
Activity Number: 325
Type: Contributed
Date/Time: Wednesday, August 6, 2003 : 8:30 AM to 10:20 AM
Sponsor: Section on Risk Analysis
Abstract - #301112
Title: Robust Estimation of Moments of Environmental Data With One-Sided Detection Limit
Author(s): Ping-Hung Hsieh*+ and Tsung-Chi Cheng
Companies: Oregon State University and National Chengchi University
Address: College of Business, Corvallis, OR, 97331-8527,
Keywords: extreme value theory ; generalized Pareto distribution ; peaks over threshold ; tail index
Abstract:

Estimation of mean and variance of environmental data is of great challenge due to the fact that often a substantial percentage of observations may lie below/above detection limits. The inadequacy of several common as well as ad hoc estimation techniques has been noted in the literature. We propose a robust moment estimator for environmental data with one-sided detection limit. The model assumes that the underlying distribution of the data or the transformed data is symmetric, and the problem of detecting reliable measurements occurs only on one side, say, the left tail. By applying the Renyi representation theorem, we utilize the observations from right (left) tail to learn the shape of the distribution below (above) detection limit without specifying any particular parametric model and, consequently, derive the moment estimator of the distribution. A comparison between the proposed estimator and several existing estimators is conducted through simulation.

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