Introduction
Maximum likelihood estimators provide a powerful statistical tool. In this paper we directly deal with non-linear reserving models, without the need to transform those models to make them tractable for linear or generalized linear methods. We also show how the same general approach can be easily adapted to provide estimates for a very wide range of reserving methods and models, making use of the same framework, and even much of the same computer code. We focus on the triangle of incre- mental average costs, and show how five common methods can be set in a stochastic framework.
For more information see: A Flexible Framework for Stochastic Reserving Models
Installation
You can install stochasticreserver from github with:
All missing dependencies should be automatically installed.
Summary of Major Functions
Quality Control
stub
Find online documentation at https://rmsharp.github.io/stochasticreserver/.
For more information see: A Flexible Framework for Stochastic Reserving Models
