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Related Experiment Video

Updated: Apr 11, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Modeling Finite-Time Failure Probabilities in Risk Analysis Applications.

Dimitrina S Dimitrova¹, Vladimir K Kaishev¹, Shouqi Zhao¹

¹Cass Business School, City University London, 106 Bunhill Row, EC1Y 8TZ, London, UK.

Risk Analysis : an Official Publication of the Society for Risk Analysis

|May 27, 2015

Summary

This study presents a framework for estimating system failure probability. The developed models offer insights into risk analysis across diverse fields like reliability and finance.

Keywords:

Alarm time Appell polynomials dam overtopping dependent risk modeling finite-time failure probability

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Last Updated: Apr 11, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

Risk Analysis
Probability Theory
System Dynamics

Background:

Assessing system failure risk is crucial for operational integrity.
Existing methods may not fully capture time-dependent risk processes.
Accurate failure probability estimation is vital for proactive risk management.

Purpose of the Study:

Introduce a novel framework for analyzing system failure risk.
Develop probabilistic models to estimate failure probability.
Provide explicit expressions for failure and excess risk probabilities.

Main Methods:

Defined a risk process characterizing system operations.
Developed two dually connected probabilistic models.
Derived explicit mathematical expressions for failure probabilities.

Main Results:

Obtained explicit expressions for failure probability.
Derived joint probabilities for failure time and risk excess.
Demonstrated applicability across multiple domains.

Conclusions:

The proposed framework provides a robust method for risk analysis.
The probabilistic models are applicable to systems reliability, inventory, flood control, disease spread, and financial insolvency.
Numerical illustrations confirm the practical utility of the framework.