 Tales from the tail: Robust estimation of moments of environmental data with one-sided detection limitsPing-Hung Hsieh Computational Statistics & Data Analysis, 2012, vol. 56, issue 12, 4266-4277 Abstract: Estimating the means and standard deviations of environmental data remains a great challenge because a substantial percentage of observations lies below or above detection limits. The inadequacy of several common, ad hoc estimation procedures is clear; this study instead proposes a robust moment estimation procedure for environmental data with a one-sided detection limit. The procedure assumes that the tails of the underlying distribution of the (transformed) data are symmetric, and censoring only occurs on one side. Through an application of the Rényi representation theorem, it is possible to use observations from the other side to learn the shape of the distribution below the detection limit, without specifying any particular parametric model, and consequently, derive the moment estimates of the distribution. A simulation provides a comparison of estimation performance between the proposed procedure and several existing estimators, and several real-life samples offer a good illustration. Keywords: Extreme value theory; Generalized Pareto distribution; Peaks over threshold; Rényi representation theorem; Tail index (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:12:p:4266-4277 DOI: 10.1016/j.csda.2012.04.007 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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