cvm.exp.test: Cramer-von Mises test for exponentiality

In exptest: Tests for Exponentiality

Description

Performs Cramer-von Mises test for the composite hypothesis of exponentiality, see e.g. Henze and Meintanis (2005, Sec. 2.1).

Usage

cvm.exp.test(x, nrepl=2000)

Arguments

x	a numeric vector of data values.

nrepl

the number of replications in Monte Carlo simulation.

Details

The Cramer-von Mises test for exponentiality is based on the following statistic:

ω^2_n =\int_0^∞ (F_n(x)-(1-\exp(-x)))^2\exp(-x)dx,

where F_n is the empirical distribution function of the scaled data Y_j=X_j/\overline{X}. The p-value is computed by Monte Carlo simulation.

Value

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

statistic	the value of the Cramer-von Mises statistic.
p.value	the p-value for the test.
method	the character string "Cramer-von Mises test for exponentiality".
data.name	a character string giving the name(s) of the data.

Author(s)

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

cvm.exp.test(rexp(100))
cvm.exp.test(runif(100, min = 50, max = 100))

Example output

	Cramer-von Mises test for exponentiality

data:  rexp(100)
Wn = 0.062571, p-value = 1


	Cramer-von Mises test for exponentiality

data:  runif(100, min = 50, max = 100)
Wn = 6.3164, p-value = 1

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