rlm.test {lawstat}

Robust L1 Moment-Based (RLM) Goodness-of-Fit Test for the Laplace Distribution

Description

Robust test for the Laplace distribution. Two options for calculating critical values, namely, approximated with Chi-square distribution and empirical, are available.

Usage

rlm.test(x, crit.values = c("chisq.approximation", "empirical"), N = 0)

Arguments

x

a numeric vector of data values.

crit.values

a character string specifying how the critical values should be obtained: approximated by the Chi-square distribution (default) or empirically.

N

number of Monte Carlo simulations for the empirical critical values.

Details

The test is based on a joint statistic using skewness and kurtosis coefficients. In particular, RLM uses the Average Absolute Deviation from the Median (MAAD), a robust estimate of standard deviation. See Gel (2010).

Value

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

statistic

the value of the test statistic.

parameter

the degrees of freedom.

p.value

the p-value of the test.

method

type of test was performed.

data.name

a character string giving the name of the data.

Author(s)

Kimihiro Noguchi, W. Wallace Hui, Yulia R. Gel

References

Gel YR (2010). “Test of fit for a Laplace distribution against heavier tailed alternatives.” Computational Statistics & Data Analysis, 54(4), 958–965. doi:10.1016/j.csda.2009.10.008.

See Also

sj.test, rjb.test, rqq, jarque.bera.test

Examples

## Laplace distributed data
x = rexp(100) - rexp(100)
rlm.test(x)