# gpdp: Posterior Distribution with Parameters of GPD In MCMC4Extremes: Posterior Distribution of Extreme Value Models in R

## Description

MCMC runs of posterior distribution of data with parameters of Generalized Pareto Distribution (GPD), with parameters sigma and xi .

## Usage

 1 gpdp(data, threshold, int=1000)

## Arguments

 data data vector threshold a threshold value int number of iteractions selected in MCMC. The program selects 1 in each 10 iteraction, then thin=10. The first thin*int/3 iteractions is used as burn-in. After that, is runned thin*int iteraction, in which 1 of thin is selected for the final MCMC chain, resulting the number of int iteractions.

## Value

An object of class gpdp that gives a list containing the points of posterior distributions of sigma and xi of the gpd distribution, the data, mean posterior, median posterior and the credibility interval of the parameters.

## Note

The joint priordistribution for these parameters is the Jeffreys prior Given as Castellanos and Cabras (2007).

## References

Castellanos, M. A. and Cabras, S. (2007). A default Bayesian procedure for the generalized Pareto distribution, Journal of Statistical Planning and Inference, 137, 473-483.

## Examples

 1 2 3 4 5 6 7 8 9 10 # Obtaining posterior distribution of a vector of simulated points x=rgpd(300,xi=0.1,mu=9,beta=2) # in this case beta is the scale parameter sigma # Obtaning 1000 points of posterior distribution ajuste=gpdp(x,9, 200) # Histogram of posterior distribution of the parameters,with 95% credibility intervals # Danish data for evir package, modelling losses over 10 ## Not run data(danish) ## Not run out=gpdp(danish,10,300)

### Example output

[1] 0.03333333
[1] 0.06666667
[1] 0.1
[1] 0.1333333
[1] 0.1666667
[1] 0.2
[1] 0.2333333
[1] 0.2666667
[1] 0.3
[1] 0.3333333
[1] 0.3666667
[1] 0.4
[1] 0.4333333
[1] 0.4666667
[1] 0.5
[1] 0.5333333
[1] 0.5666667
[1] 0.6
[1] 0.6333333
[1] 0.6666667
[1] 0.7
[1] 0.7333333
[1] 0.7666667
[1] 0.8
[1] 0.8333333
[1] 0.8666667
[1] 0.9
[1] 0.9333333
[1] 0.9666667
[1] 1

MCMC4Extremes documentation built on May 1, 2019, 8:50 p.m.