Sneh Gulati

TL;DR: In this article, the authors developed an open, public model for the purpose of probabilistic assessment of risk to insured residential property associated with wind damage from hurricanes, which comprises atmospheric science, engineering, and financial/actuarial components.

TL;DR: In this paper, the state of Florida developed an open, public model for the purpose of probabilistic assessment of risk to insured residential property associated with wind damage from hurricanes, which comprises three components; viz. the atmospheric science component, the engineering component and the actuarial science component.

TL;DR: The results show clear trends in Bayesian inference and nonparametric Inference-Genesis, which indicates that Bayesian models are more accurate than either of the other approaches.

TL;DR: In this article, for a single record-breaking sample, consistent estimation is not possible, and replication is required for global results, and the proposed distribution function and density estimators are shown to be strongly consistent and asymptotically normal as m → ∞.

TL;DR: A three‐tier architecture has been adopted to make the design and implementation of the components at each layer robust and resistant to the potential change in the lifetime of the system.