Test between non-truncated Pareto-type tails (light truncation) and truncated Pareto-type tails (rough truncation).
trTest(data, alpha = 0.05, plot = TRUE, main = "Test for truncation", ...)
Vector of
The used significance level, default is 0.05
.
Logical indicating if the P-values should be plotted as a function of FALSE
.
Title for the plot, default is "Test for truncation"
.
Additional arguments for the plot
function, see plot
for more details.
A list with following components:
Vector of the values of the tail parameter
Corresponding test values.
Critical value used for the test, i.e. qnorm(1-alpha/2)
.
Corresponding P-values.
Logical vector indicating if the null hypothesis is rejected for a certain value of k
.
We want to test
See Beirlant et al. (2016) or Section 4.2.3 of Albrecher et al. (2017) for more details.
Albrecher, H., Beirlant, J. and Teugels, J. (2017). Reinsurance: Actuarial and Statistical Aspects, Wiley, Chichester.
Beirlant, J., Fraga Alves, M.I. and Gomes, M.I. (2016). "Tail fitting for Truncated and Non-truncated Pareto-type Distributions." Extremes, 19, 429--462.
# NOT RUN {
# Sample from truncated Pareto distribution.
# truncated at 95% quantile
shape <- 2
X <- rtpareto(n=1000, shape=shape, endpoint=qpareto(0.95, shape=shape))
# Test for truncation
trTest(X)
# Sample from truncated Pareto distribution.
# truncated at 99% quantile
shape <- 2
X <- rtpareto(n=1000, shape=shape, endpoint=qpareto(0.99, shape=shape))
# Test for truncation
trTest(X)
# }
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