model, statistics, system, conditions, logic, identification, criterion, probability, gradients, Monte-Carlo, algorithm, optimization, knowledge base. " />

IDENTIFICATION CRITERIA OF CREDIT RISK MODELS BY STATISTICAL DATA

D. . Strokov, E. . Solojentsev


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Abstract

 

Application analysis of Logical-Probabilistic (LP) risk models is considered, and importance of the identification
procedure of risk LP-models by statistical data is showed. Mathematical description of risk LP-models is given.
The identification method of risk LP-models is stated and different criteria of identification are proposed and
investigated. The recommendations by their applications are given.

Keywords:   model, statistics, system, conditions, logic, identification, criterion, probability, gradients, Monte-Carlo, algorithm, optimization, knowledge base.
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