lambdadep: Estimation of the bivariate lambda function of Wadsworth and...

In mev: Modelling of Extreme Values

Estimation of the bivariate lambda function of Wadsworth and Tawn (2013)

Description

Estimation of the bivariate lambda function of Wadsworth and Tawn (2013)

Usage

lambdadep(dat, qu = 0.95, method = c("hill", "mle", "bayes"), plot = TRUE)

Arguments

dat	an n by 2 matrix of multivariate observations
qu	quantile level on uniform scale at which to threshold data. Default to 0.95
method	string indicating the estimation method
plot	logical indicating whether to return the graph of lambda The confidence intervals are based on normal quantiles. The standard errors for the hill are based on the asymptotic covariance and that of the mle derived using the delta-method. Bayesian posterior predictive interval estimates are obtained using ratio-of-uniform sampling with flat priors: the shape parameters are constrained to lie within the triangle, as are frequentist point estimates which are adjusted post-inference.

Value

a plot of the lambda function if plot=TRUE, plus an invisible list with components

• w the sequence of angles in (0,1) at which the lambda values are evaluated

• lambda point estimates of lambda

• lower.confint 95

• upper.confint 95

Examples

set.seed(12)
dat <- mev::rmev(n = 1000, d = 2, model = "log", param = 0.1)
lambdadep(dat, method = 'hill')
## Not run: 
lambdadep(dat, method = 'bayes')
lambdadep(dat, method = 'mle')
# With independent observations
dat <- matrix(runif(n = 2000), ncol = 2)
lambdadep(dat, method = 'hill')

## End(Not run)

mev documentation built on April 20, 2023, 5:10 p.m.