Ricerc@Sapienza

In many situations, a researcher is interested in the analysis of the scores of a set of observation units on a set of variables. However, in medicine, it is very frequent that the information is replicated at different occasions. The occasions can be time-varying or refer to different conditions. In such cases, the data can be stored in a 3-way array or tensor. The Candecomp/Parafac and Tucker3 methods represent the most common methods for analyzing 3-way tensors.

We propose a novel Bayesian analysis of the p-variate skew-t model, providing a new parameterization, a set of non-informative priors and a sampler specifically designed to explore the posterior density of the model parameters. Extensions, such as the multivariate regression model with skewed errors and the stochastic frontiers model, are easily accommodated. A novelty introduced in the paper is given by the extension of the bivariate skew-normal model given in Liseo and Parisi (2013) to a more realistic p-variate skew-t model.

We deal with some extensions of the space-fractional diffusion equation, which is satisfied by the density
of a stable process (see Mainardi et al. (2001)): the first equation considered here is obtained by adding an
exponential differential (or shift) operator expressed in terms of the Riesz–Feller derivative. We prove that
this produces a random component in the time-argument of the corresponding stable process, which is
represented by the so-called Poisson process with drift. Analogously, if we add, to the space-fractional

Let P be a probability on the real line generating a natural exponential family (Pt)t∈R. We show that the property that t is a median of Ptfor all t characterizes P as the standard Gaussian law N(0,1).

If α is a probability on Rd and t > 0, the Dirichlet random probability Pt - D(tα) is such that for any measurable partition (A0 , Ak) of Rd the random variable (Pt(A0) ,Pt(Ak)) is Dirichlet distributed with parameters (tα(A0), tα(Ak)). If Rd log(1 + x α(dx)

We apply to Michaelis–Menten kinetics an alternative approach to the study of Singularly Perturbed Differential Equations, that is based on the Renormalization Group (SPDERG). To this aim, we first rebuild the perturbation expansion for Michaelis–Menten kinetics, beyond the standard Quasi-Steady-State Approximation (sQSSA), determining the 2nd order contributions to the inner solutions, that are presented here for the first time to our knowledge.

Prioritization of cell cycle-regulated genes from expression time-profiles is still an open problem. The point at issue is the surprisingly poor overlap among ranked lists obtained from different experimental protocols. Instead of developing a general-purpose computational methodology for detecting periodic signals, we focus on the budding yeast mitotic cell cycle.

In this paper, we solve in the convergence set, the fractional logistic equation making use of Euler's numbers. To our knowledge, the answer is still an open question. The key point is that the coefficients can be connected with Euler's numbers, and then they can be explicitly given. The constrained of our approach is that the formula is not valid outside the convergence set. The idea of the proof consists to explore some analogies with logistic function and Euler's numbers, and then to generalize them in the fractional case.

In this paper, quantitative central limit theorems for U-statistics on the q-dimensional torus defined in the framework of the two-sample problem for Poisson processes are derived. In particular, the U-statistics are built over tight frames defined by wavelets, named toroidal needlets, enjoying excellent localization properties in both harmonic and frequency domains. The rates of convergence to Gaussianity for these statistics are obtained by means of the so-called Stein–Malliavin techniques on the Poisson space, as introduced by Peccati et al.

We study the effect of a large obstacle on the so-called residence time, i.e., the time that a particle performing a symmetric random walk in a rectangular (two-dimensional, 2D) domain needs to cross the strip. We observe complex behavior: We find out that the residence time does not depend monotonically on the geometric properties of the obstacle, such as its width, length, and position. In some cases, due to the presence of the obstacle, the mean residence time is shorter with respect to the one measured for the obstacle-free strip.