wls.gev11

Internal function that performs the core WLS steps:
(Step 3) estimate log-scale trend from absolute residuals,
(Step 4) estimate location trend from weighted data,
(Step 5) compute standardized residuals.

internal

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

wls.gev11 function

<dl><dt>xdat</dt>
<dd>Numeric vector of data.</dd>
<dt>res</dt>
<dd>Numeric vector of residuals from initial location regression.</dd>
<dt>rob</dt>
<dd>Logical. If TRUE, use robust regression. If FALSE, use OLS.</dd></dl>

Arguments

Weighted least squares core estimation for GEV11 — wls.gev11

<dl>

<dt>xdat</dt>
<dd>Numeric vector of data.</dd>


<dt>res</dt>
<dd>Numeric vector of residuals from initial location regression.</dd>


<dt>rob</dt>
<dd>Logical. If TRUE, use robust regression. If FALSE, use OLS.</dd>

</dl>

wls.gev11: Weighted least squares core estimation for GEV11

Description

Usage

Value

Arguments