co.exp.test: Test for exponentiality of Cox and Oakes

Description Usage Arguments Details Value Author(s) References Examples

Description

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

Usage

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

Arguments

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.

Details

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)).

Value

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.

Author(s)

Alexey Novikov, Ruslan Pusev and Maxim Yakovlev

References

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

Examples

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


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