A prominent problem in actuarial science is to define, or describe, premium calculation principles (pcp's) that satisfy certain properties. A frequently used resolution of the problem is achieved via distorting (e.g., lifting) the decumulative distribution function, and then calculating the...

By extending the notion of weighted premium calculation principles, we introduce weighted risk capital allocations, explore their properties, and develop computational methods. When achieving these goals, we find it particularly fruitful to relate the weighted allocations to general Stein-type...

Motivated by a real-life situation, we put forward a model and then derive an optimal strategy that maximizes the expected real-estate selling price when one of the only two remaining buyers has already made an offer but the other one is yet to make. Since the seller is not sure whether the...

We propose and develop mean-variance-ratio (MVR) statistics for comparing the performance of prospects (e.g., investment portfolios, assets, etc.) after the effect of the background risk has been mitigated. We investigate the performance of the statistics in large and small samples and show that...

We prove Strassen's law of the iterated logarithm for the Lorenz process assuming that the underlying distribution functionFand its inverseF-1are continuous, and the momentEX2+[var epsilon]is finite for some[var epsilon]0. Previous work in this area is based on assuming the existence of the...

When analyzing treatment effects, the average treatment value is frequently compared to that of the control group. This approach, naturally, is not particularly informative about specifc regions of the treatment and control distributions. For this reason and having in view a specifc application,...

We give a complete description of the rate of strong consistency of the scaled and unscaled total time on test curves, which are fundamental notions in the statistical theory of reliability and life testing. The proof is crucially based on the general Vervaat process.

We consider a kernel-type nonparametric estimator of the intensity function of a cyclic Poisson process when the period is unknown. We assume that only a single realization of the Poisson process is observed in a bounded window which expands in time. We compute the asymptotic bias, variance, and...