lmom.diff

L-moments of a distribution such as from <code>par2lmom</code>

lmomparm

L-moments of the data such as from <code>lmom.ub</code>

lmomdata

Logical switch on verbosity of output.  Default is <code>TRUE</code>.

verbose

This function computes the difference between the L-moments derived from a
parameterized distribution and the L-moments as computed from the data.  This
function is useful to characterize the bias that develops between the theoretical
L-moments of a distribution and the L-moments of the data.  This function also
is an important test on the algorithms that fit distributions to the L-moments.
The difference is computed as the L-moment from the distribution minus the
L-moment of the data.

distribution

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. 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. This package 
 is oriented around the FORTRAN algorithms of J.R.M. Hosking, and the 
 nomenclature for many of the functions parallels that of the Hosking library. 
 Numerous additional features are added to aid in extension of the breadth of 
 L-moment application. Much theoretical extension of L-moment theory has 
 occurred in recent years. E.A.H. Elamir and A.H. Seheult have developed the 
 trimmed L-moments, which are implemented in this package. Further, recent 
 developments by Robert Serfling and Peng Xiao have extended L-moments into 
 multivariate space; the so-called sample L-comoments are implemented here. The 
 supported distributions with moment type shown as L (L-moments) or TL (trimmed 
 L-moments) include the Cauchy(TL), Exponential(L), Gamma(L), Generalized Extreme 
 Value(L), Generalized Lambda(L/TL), Generalized Logistic (L), Generalized 
 Normal(L), Generalized Pareto(L/TL), Gumbel(L), Normal(L), Kappa(L), Pearson 
 Type III(L), and Wakeby(L).

lmom.diff: Difference Between L-moments of the Distribution and the L-moments of the Data

Description

Usage

Arguments

Value

See Also

Examples