claims

A dataset containing personal injury insurance claims made in Australia
from January 1998 to June 1999.

datasets

Set of tools to fit a linear multiple or semi-parametric regression
models with the possibility of non-informative random right or left censoring.
Under this setup, the localization parameter of the response variable distribution is modeled by using linear multiple regression
or semi-parametric functions, whose non-parametric components may be approximated
by natural cubic spline or P-splines. The supported distribution for the model error is a generalized log-gamma distribution which includes
the generalized extreme value and standard normal distributions as important special cases. Inference is based on likelihood, penalized likelihood and bootstrap methods.
Lastly, some numerical and graphical devices for diagnostic of the fitted models are offered.

Carlos Cardozo Delgado

sglg

Fitting Semi-Parametric Generalized log-Gamma Regression Models

Carlos Alberto Cardozo Delgado

Gilberto Paula 

Luis  Vanegas 

claims function

A data frame with 769 rows and 4 variables:<dl>
<dt>amount</dt>
<dd>amount of paid money by an insurance policy or claim size, in Australian dollars</dd><dt>legrep</dt>
<dd>with legal representation or not</dd><dt>month</dt>
<dd>month of occurrence of the accident</dd><dt>optime</dt>
<dd>operational time in percentage with range 0 to 100</dd>
</dl>

Format

Personal Injury Insurance Claims — claims

A data frame with 769 rows and 4 variables:<dl>
<dt>amount</dt>
<dd>amount of paid money by an insurance policy or claim size, in Australian dollars</dd>

<dt>legrep</dt>
<dd>with legal representation or not</dd>

<dt>month</dt>
<dd>month of occurrence of the accident</dd>

<dt>optime</dt>
<dd>operational time in percentage with range 0 to 100</dd>


</dl>

claims: Personal Injury Insurance Claims

Description

Usage

Arguments

Format