EMlogngpdmix: Mixture estimation via EM

In lognGPD: Estimation of a Lognormal - Generalized Pareto Mixture

Mixture estimation via EM

Description

This function estimates a static lognormal - generalized Pareto mixture by means of the EM algorithm. Optionally, bootstrap standard errors are computed via parallel computing.

Usage

EMlogngpdmix(x0, y, maxiter, nboot = 0)

Arguments

x0	numerical vector (5x1): initial values of the parameters p, \mu, \sigma, \xi, \beta.
y	vector: observed data.
maxiter	positive integer: maximum number of iterations of the EM algorithm.
nboot	positive integer: number of bootstrap replications for the computation of the standard errors (defaults to 0).

Value

A list with the following elements is returned: "p" = estimated value of p, "post" = posterior probabilities of all observations, "mu" = estimated value of \mu, "sigma " = estimated value of \sigma, "xi" = estimated value of \xi, "beta" = estimated value of \beta, "loglik" = maximimzed log-likelihood, "nit" = number of iterations, bootEst = matrix of parameter estimates at each bootstrap replications (only if nboot > 0). bootStd = bootstrap standard errors of each parameter (only if nboot > 0).

Examples

y <- rlognGPD(100,.9,0,1,0.5,2)
x0 <- c(.7,.2,1.3,.8,1.7)
res <- EMlogngpdmix(x0, y, 1000)

lognGPD documentation built on June 8, 2025, 11:05 a.m.