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exptest (version 1.2)

harris.exp.test: Harris modification of Gnedenko F-test

Description

Performs Harris modification of Gnedenko F-test for the composite hypothesis of exponentiality, see e.g. Ascher (1990).

Usage

harris.exp.test(x, R=length(x)/4, simulate.p.value=FALSE, nrepl=2000)

Arguments

x
a numeric vector of data values.
R
a parameter of the test (see below).
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 "Harris modification of Gnedenko F-test".
  • data.namea character string giving the name(s) of the data.

Details

The test is based on the following statistic: $$Q_n(R) =\frac{\left(\sum_{i=1}^RD_i+\sum_{i=n-R+1}^nD_i\right)/(2R)}{\sum_{i=R+1}^{n-R}D_i/(n-2R)},$$ 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, $Q_n(R)$ has an F distribution with $4R$ and $2(n-2R)$ 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
harris.exp.test(rexp(100))
harris.exp.test(rlnorm(100))

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