Kuiper test providing a comparison of a fitted distribution
with the empirical distribution.
Usage
v.test(x, distn, fit, H = NA, sim = 100, tol = 1e-04, estfun = NA)
Arguments
x
a numeric vector of data values
distn
character string naming the null distribution
fit
list of distribution parameters
H
a treshold value
sim
maximum number of szenarios in the Monte-Carlo simulation
tol
if the difference of two subsequent p-value calculations is lower than tol the
Monte-Carlo simulation stops
estfun
an function as character string or NA (default). See mctest.
Value
A list with class "mchtest" containing the following components
statistic
the value of the Kuiper statistic
treshold
the treshold value
p.value
the p-value of the test
data.name
a character string giving the name of the data
method
the character string "Kuiper test"
sim.no
number of simulated szenarios in the Monte-Carlo simulation
Details
The Kolmogorov-Smirnov test compares the null distribution with the empirical distribution
of the observed data, where left truncated data samples are allowed.
The test statistic (see ks.test) is given
by $KS = max(KS+, KS-)$.
References
Chernobay, A., Rachev, S., Fabozzi, F. (2005), Composites goodness-of-fit tests
for left-truncated loss samples, Tech. rep., University of Calivornia Santa Barbara