J
James R. Wallis
Researcher at IBM
Publications - 54
Citations - 13303
James R. Wallis is an academic researcher from IBM. The author has contributed to research in topics: Quantile & Generalized extreme value distribution. The author has an hindex of 34, co-authored 54 publications receiving 12571 citations. Previous affiliations of James R. Wallis include National Academy of Sciences.
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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.
Journal ArticleDOI
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.
Journal ArticleDOI
Probability Weighted Moments: Definition and Relation to Parameters of Several Distributions Expressable in Inverse Form
TL;DR: In this article, Probability weighted moments are introduced and shown to be potentially useful in expressing the parameters of these distributions, such as Tukey's lambda, which may present problems in deriving their parameters by more conventional means.
Journal ArticleDOI
Noah, Joseph, and Operational Hydrology
TL;DR: In this paper, a series of investigations on self-similar operational hydrology are presented, and the present paper introduces and summarizes the results of these studies. But, as a replacement for statistical hydrological models, selfsimilar models appear very promising, and they account particularly well for the remarkable empirical observations of Harold Edwin Hurst.
Journal ArticleDOI
Robustness of the rescaled range R/S in the measurement of noncyclic long run statistical dependence
TL;DR: In this article, the rescaled range R(t, s) / S t, s is shown to be a very robust statistic for testing the presence of noncyclic long run statistical dependence in records and, in cases where such dependence is present, for estimating its intensity.