ddst.evd.test | R Documentation |
Performs data driven smooth test for composite hypothesis of extreme value distribution.
Null density is given by
f(z;\gamma)=1/\gamma_2 \exp((z-\gamma_1)/\gamma_2- \exp((z-\gamma_1)/\gamma_2))
, z \in R
.
ddst.evd.test(
x,
base = ddst.base.legendre,
d.n = 10,
c = 100,
nr = 1e+05,
compute.p = TRUE,
alpha = 0.05,
compute.cv = TRUE,
...
)
x |
a (non-empty) numeric vector of data values |
base |
a function which returns an orthonormal system, possible choice: |
d.n |
an integer specifying the maximum dimension considered, only for advanced users |
c |
a calibrating parameter in the penalty in the model selection rule |
nr |
an integer specifying the number of runs for a p-value and a critical value computation if any |
compute.p |
a logical value indicating whether to compute a p-value or not |
alpha |
a significance level |
compute.cv |
a logical value indicating whether to compute a critical value corresponding to the significance level alpha or not |
... |
further arguments |
We model alternatives similarly as in Kallenberg and Ledwina (1997) and Janic-Wroblewska (2004) using Legendre's polynomials or cosines. For more details see: http://www.biecek.pl/R/ddst/description.pdf.
An object of class htest
statistic |
the value of the test statistic. |
parameter |
the number of choosen coordinates (k). |
method |
a character string indicating the parameters of performed test. |
data.name |
a character string giving the name(s) of the data. |
p.value |
the p-value for the test, computed only if |
Hosking, J.R.M., Wallis, J.R., Wood, E.F. (1985). Estimation of the generalized extreme-value distribution by the method of probability-weighted moments. Technometrics
27, 251–261.
Janic-Wroblewska, A. (2004). Data-driven smooth test for extreme value distribution. Statistics
38, 413–426.
Janic, A. and Ledwina, T. (2008). Data-driven tests for a location-scale family revisited. J. Statist. Theory. Pract. Special issue on Modern Goodness of Fit Methods. accepted.
.
Kallenberg, W.C.M., Ledwina, T. (1997). Data driven smooth tests for composite hypotheses: Comparison of powers. J. Statist. Comput. Simul.
59, 101–121.
library(evd)
set.seed(7)
# for given vector of 19 numbers
z <- c(13.41, 6.04, 1.26, 3.67, -4.54, 2.92, 0.44, 12.93, 6.77, 10.09,
4.10, 4.04, -1.97, 2.17, -5.38, -7.30, 4.75, 5.63, 8.84)
## Not run:
t <- ddst.evd.test(z, compute.p = TRUE, d.n = 10)
t
plot(t)
# H0 is true
x <- -qgumbel(runif(100),-1,1)
t <- ddst.evd.test (x, compute.p = TRUE, d.n = 10)
t
plot(t)
# H0 is false
x <- rexp(80,4)
t <- ddst.evd.test (x, compute.p = TRUE, d.n = 10)
t
plot(t)
## End(Not run)
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