gnedenko.exp.test: Gnedenko F-test of exponentiality

In exptest: Tests for Exponentiality

Description

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

Usage

gnedenko.exp.test(x, R=length(x)/2, 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.

Details

The test is based on the following statistic:

Q_n(R) =\frac{∑_{i=1}^RD_i/R}{∑_{i=R+1}^nD_i/(n-R)},

where D_i=(n-i+1)(X_{(i)}-X_{(i-1)}), X_{(0)}=0 and X_{(1)}≤q…≤q X_{(n)} are the order statistics. Under exponentiality, Q_n(R) has an F distribution with 2R and 2(n-R) degrees of freedom.

Value

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

statistic	the value of the test statistic.
p.value	the p-value for the test.
method	the character string "Gnedenko's F-test of exponentiality".
data.name	a character string giving the name(s) of the data.

Author(s)

Alexey Novikov, Ruslan Pusev and Maxim Yakovlev

References

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

Examples

gnedenko.exp.test(rexp(100))
gnedenko.exp.test(rweibull(100,2))

exptest documentation built on May 1, 2019, 8:01 p.m.