init.glme

Generates multiple random starting parameter sets for multi-start
optimization in GLME estimation. Uses L-moment estimates as a base
and adds random perturbations.

Provides generalized L-moments estimation methods for the
generalized extreme value ('GEV') distribution. Implements both stationary
'GEV' and non-stationary 'GEV11' models where location and scale parameters
vary with time. Includes various penalty functions ('Martins'-'Stedinger',
Park, Cannon, 'Coles'-Dixon) for shape parameter regularization.
Also provides model averaging estimation ('ma.gev') that combines MLE and
L-moment methods with multiple weighting schemes for robust high quantile
estimation. The 'GLME' methodology is described in Shin et al. (2025a)
<doi:10.48550/arXiv.2512.20385>. The non-stationary L-moment method
is based on Shin et al. (2025b) <doi:10.1007/s42952-025-00325-3>.
The model averaging method is described in Shin et al. (2026)
<doi:10.1007/s00477-025-03167-x>.
See also 'Hosking' (1990) <doi:10.1111/j.2517-6161.1990.tb01775.x> for
L-moments theory and 'Martins' and 'Stedinger' (2000)
<doi:10.1029/1999WR900330> for penalized likelihood methods.

Yonggwan Shin

GLmom

Generalized L-Moments Estimation for Extreme Value Distributions

Seokkap Ko

Jihong Park

Yire Shin

Jeong-Soo Park

init.glme function

<dl><dt>xdat</dt>
<dd>A numeric vector of data to be fitted.</dd>
<dt>ntry</dt>
<dd>Number of initial parameter sets to generate.</dd></dl>

Arguments

Yonggwan Shin, Seokkap Ko, Jihong Park, Yire Shin, Jeong-Soo Park

Author

Initialize random starting values for GLME optimization — init.glme

<dl>

<dt>xdat</dt>
<dd>A numeric vector of data to be fitted.</dd>


<dt>ntry</dt>
<dd>Number of initial parameter sets to generate.</dd>

</dl>

init.glme: Initialize random starting values for GLME optimization

Description

Usage

Value

Arguments

Author

See Also

Examples