J
Jonathan R. M. Hosking
Researcher at IBM
Publications - 69
Citations - 12696
Jonathan R. M. Hosking is an academic researcher from IBM. The author has contributed to research in topics: L-moment & Estimator. The author has an hindex of 29, co-authored 69 publications receiving 11886 citations. Previous affiliations of Jonathan R. M. Hosking include University of Connecticut & University of Southampton.
Papers
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Journal ArticleDOI
L-Moments: Analysis and Estimation of Distributions Using Linear Combinations of Order Statistics
TL;DR: The authors define L-moments as the expectations of certain linear combinations of order statistics, which can be defined for any random variable whose mean exists and form the basis of a general theory which covers the summarization and description of theoretical probability distributions.
MonographDOI
Regional Frequency Analysis: An Approach Based on L-Moments
TL;DR: In this paper, the authors present a regional L-moments algorithm for detecting homogeneous regions in a set of homogeneous data points and then select a frequency distribution for each region.
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Estimation of the generalized extreme-value distribution by the method of probability-weighted moments
TL;DR: In this paper, the authors use the method of probability-weighted moments to derive estimators of the parameters and quantiles of the generalized extreme-value distribution, and investigate the properties of these estimators in large samples via asymptotic theory, and in small and moderate samples, via computer simulation.
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Parameter and quantile estimation for the generalized pareto distribution
TL;DR: In this paper, the authors show that unless the sample size is 500 or more, estimators derived by either the method of moments or probability-weighted moments are more reliable.
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Some statistics useful in regional frequency analysis
TL;DR: In this article, the authors describe three statistics useful in regional frequency analysis: a discordancy measure, for identifying unusual sites in a region, a heterogeneity measure, assessing whether a proposed region is homogeneous, and a goodness-of-fit measure, which assesses whether a candidate distribution provides an adequate fit to the data.