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The beta (type 1) and inverted beta distributions are of vital importance in science, engineering and economics. In this article, we introduce a matrix variate generalization of the inverted beta distribution. By using linear and quadratic transformations, we obtain several matrix variate distributions including matrix variate generalized beta type 1 distribution. [ABSTRACT FROM AUTHOR]

We introduce a quantile-based multivariate log-normal distribution, providing a new multivariate skewed distribution with positive support. The parameters of this distribution are interpretable in terms of quantiles of marginal distributions and associations between pairs of variables, a desirable feature for statistical modeling purposes. We derive statistical properties of the quantile-based multivariate log-normal distribution involving the transformations, closed-form expressions for the mixed moments, expected value, covariance matrix, mode, Shannon entropy, and Kullback–Leibler divergence. We also present results on marginalization, conditioning, and independence. Additionally, we discuss parameter estimation and verify its performance through simulation studies. We evaluate the model fitting based on Mahalanobis-type distances. An application to children data is presented. [ABSTRACT FROM AUTHOR]

The complex Wishart distribution has ample applications in science and engineering. In this paper, we give explicit expressions for E (tr (W h)) g (tr (W j)) i and E (tr (W − h)) g (tr (W − j)) i , respectively, for particular values of g, h, i, j, g + h + i + j ≤ 5 , where W follows a complex Wishart distribution. For specific values of g, h, i, j, we first write (tr (W h)) g (tr (W j)) i and (tr (W − h)) g (tr (W − j)) i in terms of zonal polynomials and then by using results on integration evaluate resulting expressions. Several expected values of matrix-valued functions of a complex Wishart matrix have also been derived. [ABSTRACT FROM AUTHOR]

In this paper, we study a variation of the accessibility percolation model. This work is also motivated by evolutionary biology and evolutionary computation. Consider a tree whose vertices are labeled with random numbers. We study the probability of having a monotone subsequence of a path from the root to a leaf, where any k consecutive vertices in the path contain at least one vertex of the subsequence. An n-ary tree, with height h, is a tree whose vertices at distance at most h-1 to the root have n children. For the case of n-ary trees, we prove that, as h tends to infinity, the probability of having such subsequence: tends to 1, if n grows significantly faster than h/(ek)k; and tends to 0, if n grows significantly slower than h/(ek)k. [ABSTRACT FROM AUTHOR]

We investigate a metapopulation model referring to populations that are spatially structured in colonies. Each colony thrives during a random time until a catastrophe when only a random amount of individuals of that colony survives. These survivors try independently establishing new colonies at neighbour sites, randomly. If the chosen site is occupied, that individual dies, otherwise the individual founds there a new colony. Here we consider this metapopulation model subject to two schemes: (i) Poisson growth, during an exponential time, for each colony and geometric catastrophe, and (ii) Yule growth, during an exponential time, for each colony and binomial catastrophe. We study conditions on the set of parameters for these processes to survive, present relevant bounds for the probability of survival, for the number of vertices that were colonized and for the reach of the colonies compared to the starting point. As a byproduct we study convergence of sequence of branching processes. [ABSTRACT FROM AUTHOR]

In this article, we generalize the univariate Macdonald distribution to the matrix case and give its derivation using matrix variate gamma distribution. We study several properties such as cumulative distribution function, marginal distribution of submatrix, triangular factorization, moment generating function, and expected values of several functions of the Macdonald matrix. Some of these results are expressed in terms of special functions of matrix arguments and zonal polynomials. [ABSTRACT FROM PUBLISHER]

Abstract: The gamma and beta functions have been generalized in several ways. The multivariate beta and multivariate gamma functions due to Ingham and Siegel have been defined as integrals having the integrand as a scalar function of the real symmetric matrix. In this article, we define extended matrix variate gamma and extended matrix variate beta functions thereby generalizing multivariate gamma and multivariate beta functions defined by Ingham and Siegel. We study a number of properties of these newly defined functions. We also give some applications of these functions to statistical distribution theory. [Copyright &y& Elsevier]

In this article, we define a four parameter bivariate distribution whose marginal distributions are inverted beta and gamma. For this distribution, we derive results such as product moments, correlation coefficient, marginal and conditional distributions, distributions of sum, product and quotient, information matrix, and entropies. We also deal with the problem of estimation of parameters. [ABSTRACT FROM AUTHOR]

We consider stochastic growth models to represent population dynamics subject to geometric catastrophes. We analyze different dispersion schemes after catastrophes, to study how these schemes impact the population viability and comparing them with the scheme where there is no dispersion. In the schemes with dispersion, we consider that each colony, after the catastrophe event, has d new positions to place its survivors. We find out that when d = 2 no type of dispersion considered improves the chance of survival, at best it matches the scheme where there is no dispersion. When d = 3 , based on the survival probability, we conclude that dispersion may be an advantage or not, depending on its type, the rate of colony growth and the probability that an individual will survive when exposed to a catastrophe. [ABSTRACT FROM AUTHOR]

Cator and Van Mieghem [Phys. Rev. E 89, 052802 (2014)] stated that the correlation of infection at the same time between any pair of nodes in a network is non-negative for the Markovian susceptible-infected-susceptible (SIS) and susceptible-infected-removed (SIR) epidemic models. The arguments used to obtain this result rely strongly on the graphical construction of the stochastic process, as well as the Fortuin, Kasteleyn, and Ginibre (FKG) inequality. In this Comment, we show that although the approach used by the authors applies to the SIS model, it cannot be used for the SIR model as stated in their work. In particular, we observe that monotonicity in the process is crucial for invoking the FKG inequality. Moreover, we provide an example of a simple graph for which the nodal infection in the SIR Markovian model is negatively correlated. [ABSTRACT FROM AUTHOR]

We consider stochastic growth models to represent population subject to catastrophes. We analyze the subject from different set ups considering or not spatial restrictions, whether dispersion is a good strategy to increase the population viability. We find out it strongly depends on the effect of a catastrophic event, the spatial constraints of the environment and the probability that each exposed individual survives when a disaster strikes. [ABSTRACT FROM AUTHOR]