gini.exp.test: Test for exponentiality based on the Gini statistic
In exptest: Tests for Exponentiality

Description Usage Arguments Details Value Author(s) References Examples

Performs test for the composite hypothesis of exponentiality based on the Gini statistic, see e.g. Gail and Gastwirth (1978).

1	gini.exp.test(x, simulate.p.value=FALSE, nrepl=2000)

`x`	a numeric vector of data values.

`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.

The test is based on the Gini statistic

G_n = \frac{∑_{i,j=1}^n |X_i-X_j|}{2n(n-1)\overline{X}}.

Under exponentiality, the normalized statistic (12(n-1))^{1/2}(G_n-0.5) is asymptotically standard normal (see, e.g., Gail and Gastwirth (1978)).

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

`statistic`	the value of the Gini statistic.
`p.value`	the p-value for the test.
`method`	the character string "Test for exponentiality based on the Gini statistic".
`data.name`	a character string giving the name(s) of the data.

Alexey Novikov, Ruslan Pusev and Maxim Yakovlev

Gail, M.H. and Gastwirth, J.L. (1978): A scale-free goodness-of-fit test for the exponential distribution based on the Gini statistic. — J. R. Stat. Soc. Ser. B, vol. 40, No. 3, pp. 350–357.

1 2	gini.exp.test(rexp(100)) gini.exp.test(runif(100, min = 0, max = 1))

	Test for exponentiality based on the Gini statistic

data:  rexp(100)
Gn = 0.45513, p-value = 0.122


	Test for exponentiality based on the Gini statistic

data:  runif(100, min = 0, max = 1)
Gn = 0.32778, p-value = 2.921e-09