An Individual Risk Model for a Short Period

2014 ◽  
pp. 161-218
Keyword(s):  
Risk Model ◽  
Individual Risk ◽  
Short Period

scholarly journals A Functional Approach to Approximations for the Individual Risk Model

2004 ◽  
Vol 34 (2) ◽  
pp. 379-397 ◽  
Author(s):  
Susan M. Pitts
Keyword(s):  
Negative Binomial ◽  
Risk Model ◽  
Order Approximation ◽  
Functional Approach ◽  
Individual Risk ◽  
Claim Amount ◽  
Total Claim Amount ◽  
Total Claim ◽  
Binomial Approximation ◽  
The Individual

A functional approach is taken for the total claim amount distribution for the individual risk model. Various commonly used approximations for this distribution are considered, including the compound Poisson approximation, the compound binomial approximation, the compound negative binomial approximation and the normal approximation. These are shown to arise as zeroth order approximations in the functional set-up. By taking the derivative of the functional that maps the individual claim distributions onto the total claim amount distribution, new first order approximation formulae are obtained as refinements to the existing approximations. For particular choices of input, these new approximations are simple to calculate. Numerical examples, including the well-known Gerber portfolio, are considered. Corresponding approximations for stop-loss premiums are given.

scholarly journals Error Bounds for Compound Poisson Approximations of the Individual Risk Model

1992 ◽  
Vol 22 (2) ◽  
pp. 135-148 ◽  
Author(s):  
Nelson De Pril ◽  
Jan Dhaene
Keyword(s):  
Risk Model ◽  
General Setting ◽  
Poisson Model ◽  
Individual Risk ◽  
Compound Poisson ◽  
Aggregate Claims ◽  
The Individual ◽  
Stop Loss ◽  
Work Done

AbstractThe approximation of the individual risk model by a compound Poisson model plays an important role in computational risk theory. It is thus desirable to have sharp lower and upper bounds for the error resulting from this approximation if the aggregate claims distribution, related probabilities or stop-loss premiums are calculated.The aim of this paper is to unify the ideas and to extend to a more general setting the work done in this connection by Bühlmann et al. (1977), Gerber (1984) and others. The quality of the presented bounds is discussed and a comparison with the results of Hipp (1985) and Hipp & Michel (1990) is made.

The promises and challenges of using gene mutations for patient stratification in follicular lymphoma

Blood ◽  
2017 ◽  
Vol 130 (13) ◽  
pp. 1491-1498 ◽  
Author(s):  
Oliver Weigert ◽  
David M. Weinstock
Keyword(s):  
Follicular Lymphoma ◽  
Risk Model ◽  
Model Development ◽  
Gene Mutations ◽  
Predictive Biomarkers ◽  
Individual Risk ◽  
Patient Stratification ◽  
Risk Models ◽  
Risk Patients ◽  
Selection Of

Abstract Follicular lymphoma (FL) is a clinically and molecularly highly heterogeneous disease. Most patients achieve long-lasting remissions and have excellent overall survival (OS) with current treatment. However, ∼20% of patients have early progression of disease and short OS. At present, therapies are not guided by individual risk or disease biology. Reliable tools for patient stratification are urgently needed to avoid overtreatment of low-risk patients and to prioritize alternative approaches in high-risk patients. A rapidly expanding repertoire of promising therapeutic options is available for clinical evaluation; however, the numbers of patients with FL and the resources to conduct adequately powered trials are limited. Recent studies have shown that gene mutations can serve as prognostic and/or predictive biomarkers, in particular when integrated into composite risk models. Before translating these findings into routine clinical practice, however, several challenges loom. We review aspects of “clinicogenetic” risk model development and validation that apply to FL and more generally to other cancers. Finally, we propose a crowdsourcing effort that could expedite the development, validation, refinement, and selection of risk models. A new era of collaboration and harmonization is required if we hope to transition from empiric selection of therapeutics to risk-based, biology-guided treatment of patients with FL.

Individual Risk Model

2004 ◽  
Author(s):  
Jan Dhaene ◽  
David Vyncke
Keyword(s):  
Risk Model ◽  
Individual Risk
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