exptest (version 1.2)

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

Description

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

Usage

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.

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.

Details

The Cox and Oakes test is a test for the composite hypothesis of exponentiality. The test statistic is $$CO_n = n+\sum_{j=1}^n(1-Y_j)\log Y_j,$$ where $Y_j=X_j/\overline{X}$. $(6/n)^{1/2}(CO_n/\pi)$ is asymptotically standard normal (see, e.g., Henze and Meintanis (2005, Sec. 2.5)).

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

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