On the Linear Combinations of Stochastic Variables.
On the micture of some sontinuous distributions
On the multivariate skew-normal distribution and its scale mixtures.
On the number of local extrema in a sequence of independent random variables
On the number of word occurrences in a semi-Markov sequence of letters
Let a finite alphabet Ω. We consider a sequence of letters from Ω generated by a discrete time semi-Markov process We derive the probability of a word occurrence in the sequence. We also obtain results for the mean and variance of the number of overlapping occurrences of a word in a finite discrete time semi-Markov sequence of letters under certain conditions.
On the probability that integers chosen according to the binomial distribution are relative prime
On the product and ratio of Bessel random variables.
On the product and ratio of Laplace and Bessel random variables.
On the properties of the Generalized Normal Distribution
The target of this paper is to provide a critical review and to enlarge the theory related to the Generalized Normal Distributions (GND). This three term (position, scale shape) distribution is based in a strong theoretical background due to Logarithm Sobolev Inequalities. Moreover, the GND is the appropriate one to support the Generalized entropy type Fisher's information measure.
On the quadratic Wiener functional associated with the Malliavin derivative of the square norm of Brownian sample path on interval.
On the ratio of Pearson type VII and Bessel random variables.
On the relationship between Menger spaces and Wald spaces
On the robustness of properties characterizing the normal distribution
On the spectrum of a distribution function and on unique factorization.
On the Support of the Measures in a Symmetric Convolution Semigroup.
On the supports and absolute continuity of infinitely divisible probability measures.
On the tails of the distribution of the maximum of a smooth stationary gaussian process
We study the tails of the distribution of the maximum of a stationary gaussian process on a bounded interval of the real line. Under regularity conditions including the existence of the spectral moment of order 8, we give an additional term for this asymptotics. This widens the application of an expansion given originally by Piterbarg [11] for a sufficiently small interval.
On the tails of the distribution of the maximum of a smooth stationary Gaussian process
We study the tails of the distribution of the maximum of a stationary Gaussian process on a bounded interval of the real line. Under regularity conditions including the existence of the spectral moment of order 8, we give an additional term for this asymptotics. This widens the application of an expansion given originally by Piterbarg [CITE] for a sufficiently small interval.
On the Unimodality of Discrete Probability Measures.
On the Urysohn condition and the convergence of probability distributions