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Enzo Orsingher

Researcher at Sapienza University of Rome

Publications -  194
Citations -  3642

Enzo Orsingher is an academic researcher from Sapienza University of Rome. The author has contributed to research in topics: Brownian motion & Fractional calculus. The author has an hindex of 30, co-authored 189 publications receiving 3251 citations. Previous affiliations of Enzo Orsingher include University of Salerno.

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Compositions, Random Sums and Continued Random Fractions of Poisson and Fractional Poisson Processes

TL;DR: In this article, the relation between random sums and compositions of different processes was considered and it was shown that for independent Poisson processes $N_\alpha(t)$,$N_α(N_β(t)) \overset{\text{d}}{=} \sum_{j=1}^{N_ β(t)} X_j, where the $X_j$s are Poisson random variables.
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Reflecting Random Flights

TL;DR: In this article, the authors considered random flights in a sphere with center at the origin and radius R, where reflection is performed by means of circular inversion, and obtained the explicit probability distributions of the position of the moving particle when the number of changes of direction is fixed and equal to
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Planar piecewise linear random motions with jumps

TL;DR: In this paper, the authors studied persistent piecewise linear multidimensional random motions with Gaussian and exponential distributions of jump magnitudes and obtained some useful properties and formulae of distributions of these processes.
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How the sojourn time distributions of Brownian motion are affected by different forms of conditioning

TL;DR: In this paper, the distribution of the sojourn time Γ t = meas {s : B(s)>0}, where B(t), t>0 is a Brownian motion (with or without drift), under different conditions at an intermediate time u⩽t (and possibly with an additional condition at time t).
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Higher-Order Laplace Equations and Hyper-Cauchy Distributions

TL;DR: In this article, the authors introduced new distributions which are solutions of higher-order Laplace equations and proved that their densities can be obtained by folding and symmetrizing Cauchy distributions.