# 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

 1 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

 1 2 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.