rlm.test {lawstat} | R Documentation |
This function performs the robust test for the Laplace distribution. Two options for calculating critical values, namely, approximated with chisq distribution and empirical, are available.
rlm.test(x, crit.values = c("chisq.approximation", "empirical"), N = 0)
x |
a numeric vector of data values. |
crit.values |
a character string specifying how the critical values should be obtained, i.e., approximated by the chisq-distribution (default) or empirical. |
N |
number of Monte Carlo simulations for the empirical critical values |
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.
A list with class htest containing 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. |
Kimihiro Noguchi, W. Wallace Hui, Yulia R. Gel
Gastwirth, J. L.(1982) Statistical Properties of A Measure
of Tax Assessment Uniformity, Journal of Statistical Planning
and Inference 6, 1-12.
Gel, Y. R. (2009) Test of fit for a Laplace distribution against heavier tailed alternatives, Working paper.
sj.test
, rjb.test
, rqq
, jarque.bera.test
(in tseries package).
## Laplace distributed data x = rexp(100)-rexp(100) rlm.test(x) ## Sample Output ## ## Robust L1 moment-based goodness-of-fit test using a Chi-squared approximated ## critical values ## ## data: x ## Chi-squared statistic = 0.3945, df = 2, p-value = 0.821