ep.exp.test: Test for exponentiality of Epps and Pulley
In exptest: Tests for Exponentiality

Description Usage Arguments Details Value Author(s) References Examples

Performs Epps and Pulley test for the composite hypothesis of exponentiality, see e.g. Henze and Meintanis (2005, Sec. 2.8.1).

1	ep.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 Epps and Pulley test is a test for the composite hypothesis of exponentiality. The test statistic is

EP_n = (48n)^{1/2} ≤ft(\frac1n∑_{j=1}^n\exp(-Y_j)-\frac12\right),

where Y_j=X_j/\overline{X}. EP_n is asymptotically standard normal (see, e.g., Henze and Meintanis (2005, Sec. 2.8.1).

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

`statistic`	the value of the Epps and Pulley statistic.
`p.value`	the p-value for the test.
`method`	the character string "The test for exponentiality of Epps and Pulley".
`data.name`	a character string giving the name(s) of the data.

Alexey Novikov, Ruslan Pusev and Maxim Yakovlev

Henze, N. and Meintanis, S.G. (2005): Recent and classical tests for exponentiality: a partial review with comparisons. — Metrika, vol. 61, pp. 29–45.

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

	The test for exponentiality of Epps and Pulley

data:  rexp(100)
EPn = -1.3673, p-value = 0.1715


	The test for exponentiality of Epps and Pulley

data:  runif(100, min = 0, max = 1)
EPn = -5.1312, p-value = 2.878e-07