L-moments, Trimmed L-moments, L-comoments, Censored L-moments,
and Many Distributions
Description
The package implements the statistical theory of L-moments
including L-moment estimation, probability-weighted moment
estimation, parameter estimation for numerous familiar and
not-so-familiar distributions, and L-moment estimation for the
same distributions from the parameters. L-moments are derived
from the expectations of order statistics and are linear with
respect to the probability-weighted moments; choice of either
can be made by mathematical convenience. L-moments are directly
analogous to the well-known product moments; however, L-moments
have many advantages including unbiasedness, robustness, and
consistency with respect to the product moments. The method of
L-moments can out perform the method of maximum likelihood. The
lmomco package historically is oriented around canonical
FORTRAN algorithms of J.R.M. Hosking, and the nomenclature for
many of the functions parallels that of the Hosking library,
which later became available in the lmom package. However, vast
arrays of various extensions and curiosities are added by the
author to aid and expand of the breadth of L-moment
application. Such extensions include venerable statistics as
Sen weighted mean, Gini mean difference, plotting positions,
and conditional probability adjustment. Much extension of
L-moment theory has occurred in recent years, including
extension of L-moments into right-tail and left-tail censoring
by known or unknown censoring threshold and also by indicator
variable. E.A.H. Elamir and A.H. Seheult have developed the
trimmed L-moments, which are implemented in this package.
Further, Robert Serfling and Peng Xiao have extended L-moments
into multivariate space; the so-called sample L-comoments are
implemented here and might have considerable application in
copula theory because they measure asymmetric correlation and
higher co-moments. The supported distributions with moment type
shown as L (L-moments) or TL (trimmed L-moments) and additional
support for right-tail censoring ([RC]) include: Cauchy (TL),
Exponential (L), Gamma (L), Generalized Extreme Value (L),
Generalized Lambda (L & TL), Generalized Logistic (L),
Generalized Normal (L), Generalized Pareto (L[RC] & TL), Gumbel
(L), Kappa (L), Kumaraswamy (L), Normal (L), 3-parameter
log-Normal (L), Pearson Type III (L), Rayleigh (L), Reverse
Gumbel (L[RC]), Rice/Rician (L), Truncated Exponential (L),
Wakeby (L), and Weibull (L).