fit_dist_start.MixtureDistribution

fit_dist_start

Find starting values for distribution parameters

Define distribution families and fit them to
interval-censored and interval-truncated data, where the truncation
bounds may depend on the individual observation. The defined
distributions feature density, probability, sampling and fitting
methods as well as efficient implementations of the log-density log
f(x) and log-probability log P(x0 <= X <= x1) for use in 'TensorFlow'
neural networks via the 'tensorflow' package. Allows training
parametric neural networks on interval-censored and interval-truncated
data with flexible parameterization. Applications include Claims
Development in Non-Life Insurance, e.g. modelling reporting delay
distributions from incomplete data, see Bücher, Rosenstock (2022)
<doi:10.1007/s13385-022-00314-4>.

Alexander Rosenstock

reservr

Fit Distributions and Neural Networks to Censored and Truncated
Data

fit_dist_start.MixtureDistribution function

<dl><dt>dist</dt>
<dd>A <code>Distribution</code> object.</dd>
<dt>obs</dt>
<dd>Observations to fit to.</dd>
<dt>dists_start</dt>
<dd>List of initial parameters for all component
distributions. If left empty, initialisation will be automatically performed
using <code>fit_dist_start()</code> with all observations in the support of each
respective component.</dd>
<dt>...</dt>
<dd>Additional arguments for the initialisation procedure</dd></dl>

Arguments

Find starting values for distribution parameters — fit_dist_start.MixtureDistribution

<dl>

<dt>dist</dt>
<dd>A <code>Distribution</code> object.</dd>


<dt>obs</dt>
<dd>Observations to fit to.</dd>


<dt>dists_start</dt>
<dd>List of initial parameters for all component
distributions. If left empty, initialisation will be automatically performed
using <code>fit_dist_start()</code> with all observations in the support of each
respective component.</dd>


<dt>...</dt>
<dd>Additional arguments for the initialisation procedure</dd>

</dl>

fit_dist_start.MixtureDistribution: Find starting values for distribution parameters

Description

Usage

Value

Arguments

Examples