LPfitProf: Profile likelihood estimation of a lognormal-Pareto mixture

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

Profile likelihood estimation of a lognormal-Pareto mixture

Description

This function fits a lognormal-Pareto mixture by maximizing the profile log-likelihood.

Usage

LPfitProf(y, minRank, nboot)

Arguments

y	numerical vector: random sample from the mixture.
minRank	integer: minimum possible rank of the threshold.
nboot	number of bootstrap replications used for estimating the standard errors. If omitted, no standard errors are computed.

Details

Estimation is implemented as in Bee (2022). As of standard errors, at each bootstrap replication the mixture is estimated with thresholds equal to ys(minRank), ys(minRank+1),..., ys(n), where n is the sample size and ys is the sample sorted in ascending order. The latter procedure is implemented via parallel computing. If the algorithm does not converge in 1000 iterations, a message is displayed.

Value

A list with the following elements:

xmin: estimated threshold.

prior: estimated mixing weight.

postProb: matrix of posterior probabilities.

alpha: estimated Pareto shape parameter.

mu: estimated expectation of the lognormal distribution on the lognormal scale.

sigma: estimated standard deviation of the lognormal distribution on the lognormal scale.

loglik: maximized log-likelihood.

nit: number of iterations.

npareto: estimated number of Pareto observations.

bootstd: bootstrap standard errors of the estimators.

References

\insertRef

bee24aLNPar

Examples

mixFit <- LPfitProf(TN2016,90,0)

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