L-moments and C-moments
By T. Ulrych, D. Velis, Allan D. Woodbury, Mauricio D. Sacchi. 2000
50 citations recorded by the source corpus.
Abstract It is well known that the computation of higher order statistics, like skewness and kurtosis, (which we call C-moments) is very dependent on sample size and is highly susceptible to the presence of outliers. To obviate these difficulties, Hosking (1990) has introduced related statistics called L-moments. We have investigated the relationship of these two measures in a number of different ways. Firstly, we show that probability density functions (pdf ) that are estimated from L-moments are superior estimates to those obtained using C-moments and the principle of maximum entropy. C-moments computed from these pdf's are not however, contrary to what one may have expected, better estimates than those estimated from sample statistics. L-moment derived distributions for field data examples appear to be more consistent sample to sample than pdf 's determined by conventional means. Our observations and conclusions have a significant impact on the use of the conventional maximum entropy procedure which typically uses C-moments from actual data sets to infer probabilities.
Subfields: Artificial Intelligence, Statistical and Nonlinear Physics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/b1c557cfba842790d3f11c9ff79c8ea3fd090db1
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Statistics and Probability
Citations in the corpus
- An empirical power comparison of univariate goodness-of-fit tests for normality (2010, background): “Given several disadvantages associated with the use of central moments [36,50–52], Hosking [50] advocated the use of linear combinations of the order statistics instead, termed L-moments, which are less affected by sample variability and, therefore, are more robust to outliers and better for making ”