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exptest (version 1.2)

epstein.exp.test: Epstein test for exponentiality

Description

Performs Epstein test for the composite hypothesis of exponentiality, see e.g. Ascher (1990).

Usage

epstein.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:
  • statisticthe value of the test statistic.
  • p.valuethe p-value for the test.
  • methodthe character string "Epstein test for exponentiality".
  • data.namea character string giving the name(s) of the data.

Details

The test is based on the following statistic: $$EPS_n =\frac{2n\left(\log\left(n^{-1}\sum_{i=1}^nD_i\right)-n^{-1}\sum_{i=1}^n\log(D_i)\right)}{1+(n+1)/(6n)},$$ where $D_i=(n-i+1)(X_{(i)}-X_{(i-1)})$, $X_{(0)}=0$ and $X_{(1)}\leq\ldots\leq X_{(n)}$ are the order statistics. Under exponentiality, $EPS$ is approximately distributed as a chi-square with $n-1$ degrees of freedom.

References

Ascher, S. (1990): A survey of tests for exponentiality. --- Communications in Statistics -- Theory and Methods, vol. 19, pp. 1811--1825.

Examples

Run this code
epstein.exp.test(rexp(100))
epstein.exp.test(rweibull(100,2))

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