LRExp.test | R Documentation |
Likelihood Ratio test of exponentiality vs. GPD.
LRExp.test(x,
alternative = c("lomax", "GPD", "gpd", "maxlo"),
method = c("num", "sim", "asymp"),
nSamp = 15000,
simW = FALSE)
x |
Numeric vector of positive sample values. For the POT context this should be the vector of excesses over the threshold. |
alternative |
Character string describing the alternative distribution. |
method |
Method used to compute the |
nSamp |
Number of samples for a simulation, if |
simW |
Logical. If this is set to |
The Lomax and maxlo alternatives correspond to a GPD alternative with
positive shape parameter \xi > 0
(Lomax) and GPD with
\xi < 0
(maxlo).
The asymptotic distribution of the Likelihood-ratio statistic
is known. For the GPD alternative, this is a chi-square distribution
with one df. For the Lomax alternative, this is the distribution of a
product BC
where B
and C
are two independent random
variables following a Bernoulli distribution with probability
parameter p = 0.5
and a chi-square distribution with one df.
When method
is "num"
, a numerical
approximation of the distribution is used. This method
is not unlike that used by Kozubowski et al., but a different
approximation is used. However, if x
has a length
n > 500
, the method is turned to "asymp"
.
When method
is "sim"
, nSamp
samples of the
exponential distribution with the same size as x
are drawn
and the LR statistic is computed for each sample. The p
-value
is simply the estimated probability that a simulated LR is greater
than the observed LR.
Finally when method
is "asymp"
, the asymptotic
distribution is used.
A list of results with elements statistic
, p.value
and method
. Other elements are
alternative |
Character describing the alternative hypothesis. |
W |
If |
For the Lomax alternative, the distribution of the test
statistic has mixed type: it can take any positive value as
well as the value 0
with a positive probability mass. The
probability mass is the probability that the ML estimate of the GPD
shape parameter is negative, and a good approximation of it is
provided by the pGreenwood1
function. Note that this
probability converges to its limit 0.5
very slowly, which
suggests that the asymptotic distribution provides poor results for
medium sample sizes, say < 100
.
Similarly for a maxlo alternative, the distribution of the test
statistic has mixed type: it can take any positive value as
well as the value 0
with a positive probability mass
approximately given by 1 -pGreenwood1(n)
where n
is the sample size.
Yves Deville
T.J. Kozubowski, A. K. Panorska, F. Qeadan, A. Gershunov and D. Rominger (2009) "Testing Exponentiality Versus Pareto Distribution via Likelihood Ratio" Comm. Statist. Simulation Comput. 38(1), pp. 118-139.
The approximation method used is described in the Renext Computing Details report.
Lomax
, Maxlo
, GPD
for the
alternatives used here.
set.seed(1234)
x <- rGPD(n = 50, loc = 0, scale = 1, shape = 0.1)
LRExp.test(x, method = "num")$p.value
LRExp.test(x, method = "asymp")$p.value
## Not run:
## requires much time
LRExp.test(x, method = "sim")$p.value
## End(Not run)
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