6533b7d7fe1ef96bd1268586

RESEARCH PRODUCT

On the fractional probabilistic Taylor's and mean value theorems

Antonio Di CrescenzoAlessandra Meoli

subject

Generalized Taylor’s formulaMean value theoremSurvival bounded order01 natural sciencesStochastic ordering010104 statistics & probabilityCharacterization of exponential distribution; Fractional calculus; Fractional equilibrium distribution; Generalized Taylor’s formula; Mean value theorem; Survival bounded orderFOS: MathematicsCharacterization of exponential distributionApplied mathematics0101 mathematicsMathematicsComputer Science::Information RetrievalApplied MathematicsProbability (math.PR)010102 general mathematicsProbabilistic logic60E99 26A33 26A24Fractional calculusFractional equilibrium distributionFractional calculusExponential functionDistribution (mathematics)Bounded functionMean value theorem (divided differences)Random variableAnalysisMathematics - Probability

description

In order to develop certain fractional probabilistic analogues of Taylor's theorem and mean value theorem, we introduce the nth-order fractional equilibrium distribution in terms of the Weyl fractional integral and investigate its main properties. Specifically, we show a characterization result by which the nth-order fractional equilibrium distribution is identical to the starting distribution if and only if it is exponential. The nth-order fractional equilibrium density is then used to prove a fractional probabilistic Taylor's theorem based on derivatives of Riemann-Liouville type. A fractional analogue of the probabilistic mean value theorem is thus developed for pairs of nonnegative random variables ordered according to the survival bounded stochastic order. We also provide some related results, both involving the normalized moments and a fractional extension of the variance, and a formula of interest to actuarial science. In conclusion we discuss the probabilistic Taylor's theorem based on fractional Caputo derivatives.

10.1515/fca-2016-0050http://hdl.handle.net/11386/4669412