# rppvalue: P-values for the Test of the Probability of Ruin In bootruin: A Bootstrap Test for the Probability of Ruin in the Classical Risk Process

## Description

This function provides p-values for the test of the probability of ruin using one of two different methods.

## Usage

 `1` ```rppvalue(x, method = c("bootstrap", "normal"), x.boot) ```

## Arguments

 `x` The observed values of the test statistic as numeric vector or matrix, see Details. `method` A character string determining the method used. `x.boot` The bootstrap replications that `x` should be compared with, see Details.

## Details

This function is not intended to be used by itself, but rather in combination with `rpteststat`. Hence, ideally, both `x` and `x.boot` stem from a call of the latter.

If `method = "bootstrap"`, then bootstrap p-values are computed. The values of `x` are compared to those of `x.boot`. The number of rows of `x.boot` has to match the number of columns of `x` (or its length if it is a vector). For most applications, however, `x` will be a single number and `x.boot` will be the bootstrap replications of `x`

For `method = "normal"` the p-values are computed using the asymptotic normal approximation of the test statistic. `x` can be a vector, a matrix or an array of numerics.

The elements of `x` are interpreted as statistics of separate, independent tests, and adjusting the p-values for multiple comparison may be necessary.

## Value

A numeric (vector, matrix or array) with the same dimension as `x`.

## Note

If `method = "normal"`, the argument `x.boot` is not used and a warning is issued if it is still provided.

## 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 `rpteststat` for the computation of the test statistics, `ruinprob` for computating the probability of ruin, and `rpjack` for the computation of the standard errors.
`p.adjust` from the package stats provides methods to adjust p-values for multiple testing.