bgev.mle {bgev}

Parameter estimation of bimodal GEV distribution based on real data that appears to be bimodal.

Description

Finds the maximum likelihood estimators of the bimodal GEV distribution parameters by minimizing the log-likelihood function of the data.

Usage

bgev.mle(x, lower = c(-3, 0.1, -3, -0.9), upper = c(3, 3, 3,3), itermax = 50)

Arguments

x

a unidimensional vector containing observations to estimate a bimodal GEV distribution

lower

a vector of dimension 4 containing the lower limit for each of the bimodal GEV parameters where the search is going to take place.

upper

a vector of dimension 4 containing the upper limit for each of the bimodal GEV parameters where the search is going to take place.

itermax

maximum number of interations when finding a good starting value for the optimization algorithm.

Value

A list with components returned by the optim R function for which the main values are

par

best parameters of bimodal gev fitting the data

value

value of the log-likelihood corresponding to 'par'

Author(s)

Cira Otiniano Author [aut], Yasmin Lirio Author [aut], Thiago Sousa Developer [cre]

References

OTINIANO, Cira EG, et al. (2023). A bimodal model for extremes data. Environmental and Ecological Statistics, 1-28. http://dx.doi.org/10.1007/s10651-023-00566-7

Examples

# generate 100 values distributed according to a bimodal GEV
x = rbgev(50, mu = 0.2, sigma = 1, xi = 0.5, delta = 0.2) 
# estimate the bimodal GEV parameters using the generated data
bgev.mle(x)