lorenz.exp.test: Lorenz test for exponentiality

In exptest: Tests for Exponentiality

Description

Performs Lorenz test for the composite hypothesis of exponentiality, see e.g. Gail and Gastwirth (1978).

Usage

lorenz.exp.test(x, p=0.5, simulate.p.value=FALSE, nrepl=2000)

Arguments

x	a numeric vector of data values.

p	a parameter of the test (see below).
simulate.p.value	a logical value indicating whether to compute p-values by Monte Carlo simulation.
nrepl	the number of replications in Monte Carlo simulation.

Details

The Lorenz test for exponentiality is based on the following statistic:

L = ∑_{i=1}^{np}{X_{(i)}}/∑_{i=1}^n{X_{(i)}}

The statistic √{n}(L-p-(1-p)\log(1-p)) is asymptotically standard normal.

Value

A list with class "htest" containing the following components:

statistic	the value of the Lorenz statistic.
p.value	the p-value for the test.
method	the character string "Lorenz test for exponentiality".
data.name	a character string giving the name(s) of the data.

Author(s)

Alexey Novikov and Ruslan Pusev

References

Gail, M.H. and Gastwirth, J.L. (1978): A scale-free goodness-of-fit test for the exponential distribution based on the Lorenz curve. — Journal of the American Statistical Association, vol. 73, pp. 787–793.

Examples

lorenz.exp.test(rexp(100))
lorenz.exp.test(rchisq(100,7))

exptest documentation built on May 1, 2019, 8:01 p.m.