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MultiChainLadderFit-class

Class "MultiChainLadderFit", "MCLFit" and "GMCLFit"


Description

"MultiChainLadderFit" is a virtual class for the fitted models in the multivariate chain ladder reserving framework, "MCLFit" is a result from the interal call .FitMCL to store results in model MCL and "GMCLFit" is a result from the interal call .FitGMCL to store results in model GMCL. The two classes "MCLFit" and "GMCLFit" differ only in the presentation of B_k and Σ_{B_k}, and different methods of Mse and predict will be dispatched according to these classes.

Objects from the Class

"MultiChainLadderFit" is a virtual Class: No objects may be created from it. For "MCLFit" and "GMCLFit", objects can be created by calls of the form new("MCLFit", ...) and new("GMCLFit", ...) respectively.

Slots

Triangles:

Object of class "triangles"

models:

Object of class "list"

B:

Object of class "list"

Bcov:

Object of class "list"

ecov:

Object of class "list"

fit.method:

Object of class "character"

delta:

Object of class "numeric"

int:

Object of class "NullNum"

restrict.regMat:

Object of class "NullList"

Extends

"MCLFit" and "GMCLFit" extends class "MultiChainLadderFit", directly.

Methods

No methods defined with class "MultiChainLadderFit" in the signature.

For "MCLFit", the following methods are defined:

Mse

signature(ModelFit = "MCLFit", FullTriangles = "triangles"): Calculate Mse estimations.

predict

signature(object = "MCLFit"): Predict ultimate losses and complete the triangles. The output is an object of class "triangles".

For "GMCLFit", the following methods are defined:

Mse

signature(ModelFit = "GMCLFit", FullTriangles = "triangles"): Calculate Mse estimations.

predict

signature(object = "GMCLFit"): Predict ultimate losses and complete the triangles. The output is an object of class "triangles".

Author(s)

See Also

See also Mse.

Examples

showClass("MultiChainLadderFit")

ChainLadder

Statistical Methods and Models for Claims Reserving in General Insurance

v0.2.12
GPL (>= 2)
Authors
Markus Gesmann [aut, cre], Daniel Murphy [aut], Yanwei (Wayne) Zhang [aut], Alessandro Carrato [aut], Giuseppe Crupi [ctb], Christophe Dutang [ctb], Arnaud Lacoume [ctb], Arthur Charpentier [ctb], Mario Wuthrich [aut], Fabio Concina [aut], Eric Dal Moro [aut], Yuriy Krvavych [ctb], Vincent Goulet [ctb], Marco De Virgilis [ctb]
Initial release
2021-01-05

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