ruinprob.test {bootruin} | R Documentation |
A Bootstrap Test for the Probability of Ruin in the Classical Risk Process
Description
This function provides a testing framework for the probability of ruin in the classical, compound Poisson risk process. The test can be performed using the bootstrap method or using normal approximation.
Usage
ruinprob.test(x, prob.null, type = c("bootstrap", "normal"),
nboot, bootmethod = c("nonp", "exp", "lnorm"), ...)
Arguments
x |
a numeric vector of data values (claims) |
prob.null |
a number indicating the hypothesized true probability of ruin. |
type |
a character string determining the type of test that is performed. |
nboot |
a number indicating the number of bootstrap replications. |
bootmethod |
a character string determining how the bootstrap replications are created. |
... |
further arguments to be passed to |
Details
The null hypothesis is that the probability of ruin is equal to
prob.null
versus the one-sided alternative that probability of ruin
is smaller than prob.null
.
If type = "bootstrap"
, a bootstrap test is performed. The arguments
nboot
and bootmethod
have to be specified. bootmethod
determines the kind of bootstrap: "nonp"
creates the usual
nonparametric bootstrap replications, while "exp"
and
"lnorm"
create parametric bootstrap replications, the former
assuming exponentially distributed claims, the latter log-normally
distributed ones.
type = "normal"
makes use of an asymptotic normal approximation.
The computations are a lot faster, but from a theoretical point of view
the bootstrap method is more accurate, see References.
For details about the necessary and valid arguments that might have to be
supplied for ...
, see ruinprob
.
Value
A list with class "htest"
containing the following components:
statistic |
the value of the studentized probability of ruin, i.e. the test statistic. |
parameter |
additional parameters. |
p.value |
the p-value for the test. |
estimate |
the estimated probability of ruin. |
null.value |
the specified hypothesized value of the probability of ruin. |
alternative |
a character string describing the alternative hypothesis. |
method |
a character string indicating what type of test was performed. |
data.name |
a character string giving the name of the data. |
Note
Using the bootstrap method is computationally intensive. Values for
nboot
should not be too large, usually numbers between 50 and 200
are reasonable choices.
References
Baumgartner, B. and Gatto, R. (2010) A Bootstrap Test for the Probability of Ruin in the Compound Poisson Risk Process. ASTIN Bulletin, 40(1), pp. 241–255.
See Also
Examples
# Generating a sample of 50 exponentially distributed claims with mean 10
x <- rexp(50, 0.1)
## Not run:
# Given this sample, test whether the probability of ruin is smaller than
# 0.1 using a bootstrap test with 100 bootstrap replications.
ruinprob.test(
x = x, prob.null = 0.10, type = "bootstrap",
loading = 0.2, reserve = 100, interval = 1,
bootmethod = "nonp", nboot = 100
)
## End(Not run)
# The same test using normal approximation. This is a lot faster.
ruinprob.test(
x = x, prob.null = 0.15, type = "normal",
loading = 0.2, reserve = 100, interval = 1
)