fggpd {extrememix}R Documentation

GGPD Estimation

Description

Fit of the GGPD model using an MCMC algorithm.

Usage

fggpd(x, it, start = NULL, var = NULL, prior = NULL, thin = 1, burn = 0)

Arguments

x

A vector of positive observations.

it

Number of iterations of the algorithm.

start

A list of starting parameter values.

var

A list of starting proposal variances.

prior

A list of hyperparameters for the prior distribution.

thin

Thinning interval.

burn

Burn-in length.

Details

Estimation of the GGPD is carried out using an adaptive block Metropolis-Hastings algorithm. As standard, the user needs to specify the data to use during estimation, the number of iterations of the algorithm, the burn-in period (by default equal to zero) and the thinning interval (by default equal to one). To run the algorithm it is also needed the choice of the starting values, the starting values of the proposal variances, and the parameters of the prior distribution. If not provided, these are automatically set as follows:

The user can also select any of the three inputs above.

Value

fggpd returns a list with three elements:

References

Behrens, Cibele N., Hedibert F. Lopes, and Dani Gamerman. "Bayesian analysis of extreme events with threshold estimation." Statistical Modelling 4.3 (2004): 227-244.

do Nascimento, Fernando Ferraz, Dani Gamerman, and Hedibert Freitas Lopes. "A semiparametric Bayesian approach to extreme value estimation." Statistics and Computing 22.2 (2012): 661-675.

See Also

ggpd

Examples


## Small number of iterations and burn-in for quick execution
data(rainfall)
model1 <- fggpd(rainfall, it = 250, burn = 50, thin = 25)

start <- list(xi = 0.2, sigma = 2, u = 10, mu = 5, eta = 2)
var <- list(xi = 0.01, sigma = 1, u = 3, mu = 3, eta = 1)
prior <- list(u = c(22,5), mu = c(4,16), eta = c(0.001,0.001))
model2 <- fggpd(rainfall,it = 250, start = start, var =var, prior = prior)



[Package extrememix version 0.0.1 Index]