fun.theo.mv.gld {GLDEX}

Find the theoretical first four moments of the generalised lambda distribution.

Description

Computes the "mean","variance","skewness","kurtosis" statistics from a given generalised lambda distribution.

Usage

fun.theo.mv.gld(L1, L2, L3, L4, param, normalise="N")

Arguments

L1

Lambda 1. Or c(Lambda 1,Lambda 2,Lambda 3,Lambda 4).

L2

Lambda 2.

L3

Lambda 3.

L4

Lambda 4.

param

"rs" or "fmkl" or "fkml"

normalise

"Y" if you want kurtosis to be calculated with reference to kurtosis = 0 under Normal distribution.

Value

A vector listing the values of mean, variance, skewness and kurtosis.

Note

Sometimes the theoretical moments may not exist, in those cases, NA is returned.

Author(s)

Steve Su

References

Freimer, M., Mudholkar, G. S., Kollia, G. & Lin, C. T. (1988), A study of the generalized tukey lambda family, Communications in Statistics - Theory and Methods *17*, 3547-3567.

Gilchrist, Warren G. (2000), Statistical Modelling with Quantile Functions, Chapman & Hall

Karian, Z.A., Dudewicz, E.J., and McDonald, P. (1996), The extended generalized lambda distribution system for fitting distributions to data: history, completion of theory, tables, applications, the “Final Word” on Moment fits, Communications in Statistics - Simulation and Computation *25*, 611-642.

Karian, Zaven A. and Dudewicz, Edward J. (2000), Fitting statistical distributions: the Generalized Lambda Distribution and Generalized Bootstrap methods, Chapman & Hall

See Also

fun.comp.moments.ml, fun.comp.moments.ml.2, fun.lm.theo.gld

Examples

fun.theo.mv.gld(1, 2, 3, 4, "rs")
fun.theo.mv.gld(1, 2, 3, 4, "fmkl")