gpd_k1_means

Analytical Expressions for Predictive Means
RHP mean based on the expectation of DMGS equation 2.1

Generates predictive distributions based on calibrating priors
for various commonly used statistical models, including models with
predictors. Routines for densities, probabilities, quantiles, random
deviates and the parameter posterior are provided. The predictions are
generated from the Bayesian prediction integral, with priors chosen to
give good reliability (also known as calibration). For homogeneous models,
the prior is set to the right Haar prior, giving predictions which are
exactly reliable. As a result, in repeated testing, the frequencies of
out-of-sample outcomes and the probabilities from the predictions agree.
For other models, the prior is chosen to give good reliability. Where
possible, the Bayesian prediction integral is solved exactly. Where exact
solutions are not possible, the Bayesian prediction integral is solved
using the Datta-Mukerjee-Ghosh-Sweeting (DMGS) asymptotic expansion.
Optionally, the prediction integral can also be solved using posterior
samples generated using Paul Northrop's ratio of uniforms sampling package
('rust'). Results are also generated based on maximum likelihood, for
comparison purposes. Various model selection diagnostics and testing
routines are included. Based on "Reducing reliability bias in assessments
of extreme weather risk using calibrating priors", Jewson, S., Sweeting, T.
and Jewson, L. (2024); <doi:10.5194/ascmo-11-1-2025>.

Stephen Jewson

fitdistcp

Distribution Fitting with Calibrating Priors for Commonly Used
Distributions

gpd_k1_means function

<dl><dt>means</dt>
<dd>logical that indicates whether to return analytical estimates for the distribution means (longer runtime)</dd>
<dt>ml_params</dt>
<dd>parameters</dd>
<dt>lddi</dt>
<dd>inverse observed information matrix</dd>
<dt>lddi_k2</dt>
<dd>inverse observed information matrix, fixed shape parameter</dd>
<dt>lddd</dt>
<dd>third derivative of log-likelihood</dd>
<dt>lddd_k2</dt>
<dd>third derivative of log-likelihood, fixed shape parameter</dd>
<dt>lambdad_flat</dt>
<dd>derivative of the log flat prior</dd>
<dt>lambdad_rh_mle</dt>
<dd>derivative of the log CRHP-MLE prior</dd>
<dt>lambdad_rh_flat</dt>
<dd>derivative of the log CRHP-FLAT prior</dd>
<dt>lambdad_jp</dt>
<dd>derivative of the log JP prior</dd>
<dt>nx</dt>
<dd>length of training data</dd>
<dt>dim</dt>
<dd>number of parameters</dd>
<dt>kloc</dt>
<dd>the known location parameter</dd></dl>

Arguments

Analytical Expressions for Predictive Means
RHP mean based on the expectation of DMGS equation 2.1 — gpd_k1_means

<dl>

<dt>means</dt>
<dd>logical that indicates whether to return analytical estimates for the distribution means (longer runtime)</dd>


<dt>ml_params</dt>
<dd>parameters</dd>


<dt>lddi</dt>
<dd>inverse observed information matrix</dd>


<dt>lddi_k2</dt>
<dd>inverse observed information matrix, fixed shape parameter</dd>


<dt>lddd</dt>
<dd>third derivative of log-likelihood</dd>


<dt>lddd_k2</dt>
<dd>third derivative of log-likelihood, fixed shape parameter</dd>


<dt>lambdad_flat</dt>
<dd>derivative of the log flat prior</dd>


<dt>lambdad_rh_mle</dt>
<dd>derivative of the log CRHP-MLE prior</dd>


<dt>lambdad_rh_flat</dt>
<dd>derivative of the log CRHP-FLAT prior</dd>


<dt>lambdad_jp</dt>
<dd>derivative of the log JP prior</dd>


<dt>nx</dt>
<dd>length of training data</dd>


<dt>dim</dt>
<dd>number of parameters</dd>


<dt>kloc</dt>
<dd>the known location parameter</dd>

</dl>

gpd_k1_means: Analytical Expressions for Predictive Means RHP mean based on the expectation of DMGS equation 2.1

Description

Usage

Value

Arguments