get_diagnosis

internal

An enhanced implementation of Whittaker-Henderson smoothing for the graduation
of one-dimensional and two-dimensional actuarial tables used to quantify Life Insurance risks.
'WH' is based on the methods described in Biessy (2025) <doi:10.48550/arXiv.2306.06932>.
Among other features, it generalizes the original smoothing algorithm to maximum likelihood estimation,
automatically selects the smoothing parameter(s) and extrapolates beyond the range of data.

Guillaume Biessy

Enhanced Implementation of Whittaker-Henderson Smoothing

get_diagnosis function

<dl><dt>dev</dt>
<dd>Deviance of the model</dd>
<dt>pen</dt>
<dd>Penalization force</dd>
<dt>sum_edf</dt>
<dd>Effective dimension</dd>
<dt>n_pos</dt>
<dd>Number of strictly positive weights</dd>
<dt>p</dt>
<dd>Number of parameters</dd>
<dt>q</dt>
<dd>Orders of the penalization matrices</dd>
<dt>s</dt>
<dd>eigenvalues for the penalization matrix</dd>
<dt>lambda</dt>
<dd>smoothing parameters</dd>
<dt>R</dt>
<dd>triangular factor</dd>
<dt>l_sat</dt>
<dd>likelihood for the saturated model</dd></dl>

Arguments

Diagnosis for Model Fit — get_diagnosis

<dl>

<dt>dev</dt>
<dd>Deviance of the model</dd>


<dt>pen</dt>
<dd>Penalization force</dd>


<dt>sum_edf</dt>
<dd>Effective dimension</dd>


<dt>n_pos</dt>
<dd>Number of strictly positive weights</dd>


<dt>p</dt>
<dd>Number of parameters</dd>


<dt>q</dt>
<dd>Orders of the penalization matrices</dd>


<dt>s</dt>
<dd>eigenvalues for the penalization matrix</dd>


<dt>lambda</dt>
<dd>smoothing parameters</dd>


<dt>R</dt>
<dd>triangular factor</dd>


<dt>l_sat</dt>
<dd>likelihood for the saturated model</dd>

</dl>

get_diagnosis: Diagnosis for Model Fit

Description

Usage

Value

Arguments