MLEfit: Estimating a dynamic mixture via MLE

In FitDynMix: Estimation of Dynamic Mixtures

Estimating a dynamic mixture via MLE

Description

This function fits a dynamic mixture via standard maximum likelihood. Currently only implemented for the lognormal - generalized Pareto case, with Cauchy or exponential weight.

Usage

MLEfit(yObs, bootreps, intTol = 1e-04, weight)

Arguments

yObs	numerical vector: observed sample.
bootreps	non-negative integer: number of bootstrap replications. If equal to 0, no standard errors are computed.
intTol	non-negative scalar: threshold for stopping the computation of the integral in the normalization constant: if the integral on the interval from n-1 to n is smaller than intTol, the approximation procedure stops.
weight	'cau' or 'exp': name of weight distribution.

Details

Starting values for \mu and \sigma are the lognormal MLEs computed with the observations below the median. Initial values for \xi and \tau are the GPD MLEs obtained with the observations above the median. For the location and scale parameter of the Cauchy, we respectively use the first quartile and abs(log(sd(x)/2)). For the parameter of the exponential, we use abs(log(sd(x)/2)).

Value

MLEpars vector: maximum likelihood estimates and maximized log-likelihood.

MLEboot matrix: maximum likelihood estimates obtained in each bootstrap replication.

sdMLE vector: bootstrap standard deviation of the MLEs.

References

\insertRef

bee22bFitDynMix

See Also

AMLEfit

Examples

mixFit <- MLEfit(Metro2019,0,1e-04,'cau')

FitDynMix documentation built on June 10, 2025, 9:12 a.m.