EWGoF (version 2.2.2)

CF.test: GoF tests based on the empirical characteristic function for the Exponential distribution

Description

Computes the GoF tests based on the characteristic function of the Exponential distribution: Epps-Pulley (EP), Henze-Meintanis (W1, W2) and Meintanis-Iliopoulos test statistics (T1, T2).

Usage

CF.test(x, type = "EP", a = 1, nsim = 200)

Arguments

x

a numeric vector of data values.

type

the type of the test statistic used. "EP" is the default used test of Epps-Pulley,"W1" and "W2" for Henze and Meintanis, "T1" and "T2" for Meintanis-Iliopoulos test statistics.

a

parameter value to be adjusted for the test statistics ("W1", "W2", "T1" and "T2").

nsim

an integer specifying the number of replicates used in Monte Carlo.

Value

An object of class htest.

Details

The computation time of this function is quite long for the test statistics "W1", "W2", "T1" and "T2" because of their complex expression. The Monte-Carlo simulations take more time compared to the other tests. These tests are not defined for a=0.

References

Epps T.W. and Pulley L.B., A test for exponentiality vs. monotone hazard alternatives derived from the empirical characteristic function, Journal of the Royal Statistical Society, Series B, 48, 206-213, 1986.

Henze N. and Meintanis S.G., Recent and classical tests for exponentiality: partial review with comparisons, Metrika, 61, 29-45, 2005.

Henze N. and Meintanis S.G., Goodness-of-fit tests based on a new characterization of the exponential distribution, Communications in Statistics, Theory and Methods, 31, 1479-1497, 2002.

Meintanis S.G. and Iliopoulos G., Characterizations of the exponential distribution based on certain properties of its characteristic function, Kybernetika, 39 (3), 295-298, 2003.

Examples

# NOT RUN {
x <- rgamma(10,0.3)

#Apply the Epps-Pulley test
CF.test(x,type="EP")

# Apply the test of Meintanis-Iliopoulos
CF.test(x,type="T1",nsim=200)


# Apply the test of Henze-Meintanis
CF.test(x,type="W1",nsim=200)

# }