LPfitEM: Estimating a lognormal-Pareto mixture via the ECME algorithm

In LNPar: Estimation and Testing for a Lognormal-Pareto Mixture

Estimating a lognormal-Pareto mixture via the ECME algorithm

Description

This function fits a lognormal-Pareto mixture by means of the ECME algorithm.

Usage

LPfitEM(y, eps, maxiter, qxmin0 = 0.5, nboot = 0)

Arguments

y	numerical vector: random sample from the mixture.
eps	non-negative scalar: tolerance for the stopping rule.
maxiter	non-negative integer: maximum number of iterations of the ECME algorithm.
qxmin0	scalar, 0 < qxmin0 < 1: quantile level used for determining the starting value of xmin. Defaults to 0.5.
nboot	non-negative integer: number of bootstrap replications used for estimating the standard errors. If omitted, no standard errors are computed.

Details

Estimation of a lognormal-Pareto mixture via the ECME algorithm. Standard errors are computed via non-parametric bootstrap.

Value

A list with the following elements:

pars: estimated parameters (p, alpha, mu, sigma, xmin).

loglik: maximized log-likelihood.

thRank: estimated rank of xmin.

niter: number of iterations.

npareto: estimated number of Pareto observations.

postProb: matrix of posterior probabilities.

bootEst: matrix of estimated parameters at each bootstrap replication.

bootstd: bootstrap standard errors of the estimators.

Examples

ysim <- rLnormParMix(100,.9,0,1,5,1)
mixFit <- LPfitEM(ysim,eps=1e-10,maxiter=1000,nboot=0)

LNPar documentation built on April 4, 2025, 5:07 a.m.