fmgpd {extrememix}R Documentation

MGPD Estimation

Description

Fit of the MGPD model using an MCMC algorithm.

Usage

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

Arguments

x

A vector of positive observations.

it

Number of iterations of the algorithm.

k

number of mixture components for the bulk. Must be either 2, 3, or 4.

start

A list of starting parameter values.

var

A list of starting proposal variance.

prior

A list of hyperparameters for the prior distribution.

thin

Thinning interval.

burn

Burn-in.

Details

Estimation of the MGPD 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 mixture components for the bulk, 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

fmgpd 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

fggpd, mgpd

Examples


data(rainfall)
## Small number of iterations and burn-in for quick execution
model1 <- fmgpd(rainfall, k = 2, it = 250, burn = 50, thin = 25)
start <- list(xi = 0.2, sigma = 2, u = 10, mu = c(2,5), eta = c(2,2) , w = c(0.4,0.6))
var <- list(xi = 0.01, sigma = 1, u = 3, mu = c(3,3), w = 0.01)
prior <- list(u = c(22,5), mu_mu = c(2,5), mu_eta = c(0.01,0.01),
         eta_mu = c(3,3),eta_eta = c(0.01,0.01))

model2 <- fmgpd(rainfall, k= 2, it = 250, start = start, var =var, prior = prior)



[Package extrememix version 0.0.1 Index]