predict.WH_1d

Extrapolate the model for new observations.

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

predict.WH_1d function

<dl><dt>object</dt>
<dd>An object of class <code>"WH_1d"</code> returned by the <code>WH()</code> function</dd>
<dt>newdata</dt>
<dd>A vector containing the position of new observations.
Observations from the fit will automatically be added to this, in the
adequate order</dd>
<dt>...</dt>
<dd>Not used</dd></dl>

Arguments

Predict new values using a fitted 1D WH model — predict.WH_1d

<dl>

<dt>object</dt>
<dd>An object of class <code>"WH_1d"</code> returned by the <code>WH()</code> function</dd>


<dt>newdata</dt>
<dd>A vector containing the position of new observations.
Observations from the fit will automatically be added to this, in the
adequate order</dd>


<dt>...</dt>
<dd>Not used</dd>

</dl>

predict.WH_1d: Predict new values using a fitted 1D WH model

Description

Usage

Value

Arguments

Examples