co.exp.test: Test for exponentiality of Cox and Oakes
In exptest: Tests for Exponentiality

Description Usage Arguments Details Value Author(s) References Examples

Performs Cox and Oakes test for the composite hypothesis of exponentiality, see e.g. Henze and Meintanis (2005, Sec. 2.5).

1	co.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 Cox and Oakes test is a test for the composite hypothesis of exponentiality. The test statistic is

CO_n = n+∑_{j=1}^n(1-Y_j)\log Y_j,

where Y_j=X_j/\overline{X}. (6/n)^{1/2}(CO_n/π) is asymptotically standard normal (see, e.g., Henze and Meintanis (2005, Sec. 2.5)).

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

`statistic`	the value of the Cox and Oakes statistic.
`p.value`	the p-value for the test.
`method`	the character string "Test for exponentiality based on the statistic of Cox and Oakes".
`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	co.exp.test(rexp(100)) co.exp.test(runif(100, min = 0, max = 1))

	Test for exponentiality based on the statistic of Cox and Oakes

data:  rexp(100)
COn = 15.287, p-value = 0.2333


	Test for exponentiality based on the statistic of Cox and Oakes

data:  runif(100, min = 0, max = 1)
COn = 55.118, p-value = 1.727e-05