Products Of Independent Random Variables Research Articles

Convex algebras of probability distributions induced by finite associative rings

Abstract We consider the transformations of random variables over a finite associative ring by the addition and multiplication operations. For arbitrary finite rings, we construct families of distribution algebras, which are sets of distributions closed over sums and products of independent random variables.

On eigenvector statistics in the spherical and truncated unitary ensembles

We study the overlaps between right and left eigenvectors for random matrices of the spherical ensemble, as well as truncated unitary ensembles in the regime where half of the matrix at least is truncated. These two integrable models exhibit a form of duality, and the essential steps of our investigation can therefore be performed in parallel. In every case, conditionally on all eigenvalues, diagonal overlaps are shown to be distributed as a product of independent random variables with explicit distributions. This enables us to prove that the scaled diagonal overlaps, conditionally on one eigenvalue, converge in distribution to a heavy-tail limit, namely, the inverse of a γ2 distribution. We also provide formulae for the conditional expectation of diagonal and off-diagonal overlaps, either with respect to one eigenvalue, or with respect to the whole spectrum. These results, analogous to what is known for the complex Ginibre ensemble, can be obtained in these cases thanks to integration techniques inspired from a previous work by Forrester & Krishnapur.

Multivariate Scale-Mixed Stable Distributions and Related Limit Theorems

In the paper, multivariate probability distributions are considered that are representable as scale mixtures of multivariate stable distributions. Multivariate analogs of the Mittag–Leffler distribution are introduced. Some properties of these distributions are discussed. The main focus is on the representations of the corresponding random vectors as products of independent random variables and vectors. In these products, relations are traced of the distributions of the involved terms with popular probability distributions. As examples of distributions of the class of scale mixtures of multivariate stable distributions, multivariate generalized Linnik distributions and multivariate generalized Mittag–Leffler distributions are considered in detail. Their relations with multivariate ‘ordinary’ Linnik distributions, multivariate normal, stable and Laplace laws as well as with univariate Mittag–Leffler and generalized Mittag–Leffler distributions are discussed. Limit theorems are proved presenting necessary and sufficient conditions for the convergence of the distributions of random sequences with independent random indices (including sums of a random number of random vectors and multivariate statistics constructed from samples with random sizes) to scale mixtures of multivariate elliptically contoured stable distributions. The property of scale-mixed multivariate elliptically contoured stable distributions to be both scale mixtures of a non-trivial multivariate stable distribution and a normal scale mixture is used to obtain necessary and sufficient conditions for the convergence of the distributions of random sums of random vectors with covariance matrices to the multivariate generalized Linnik distribution.

The distribution of overlaps between eigenvectors of Ginibre matrices

We study the overlaps between eigenvectors of nonnormal matrices. They quantify the stability of the spectrum, and characterize the joint eigenvalues increments under Dyson-type dynamics. Well known work by Chalker and Mehlig calculated the expectation of these overlaps for complex Ginibre matrices. For the same model, we extend their results by deriving the distribution of diagonal overlaps (the condition numbers), and their correlations. We prove: (i) convergence of condition numbers for bulk eigenvalues to an inverse Gamma distribution; more generally, we decompose the quenched overlap (i.e. conditioned on eigenvalues) as a product of independent random variables; (ii) asymptotic expectation of off-diagonal overlaps, both for microscopic or mesoscopic separation of the corresponding eigenvalues; (iii) decorrelation of condition numbers associated to eigenvalues at mesoscopic distance, at polynomial speed in the dimension; (iv) second moment asymptotics to identify the fluctuations order for off-diagonal overlaps, when the related eigenvalues are separated by any mesoscopic scale; (v) a new formula for the correlation between overlaps for eigenvalues at microscopic distance, both diagonal and off-diagonal. These results imply estimates on the extreme condition numbers, the volume of the pseudospectrum and the diffusive evolution of eigenvalues under Dyson-type dynamics, at equilibrium.

Analysis and Optimization of Wireless-Powered Cooperative Jamming for Sensor Network Over Nakagami-$m$ Fading Channels

This letter studies the secrecy communication in a wireless-powered sensor network, where the access point (AP) first sends an energy signal to power the sensor node (S) and jammer (J) and, then, J exploits the harvested energy to send a jamming signal to interfere with the eavesdropper, in order to protect the uplink communication from S to the AP. Specifically, assuming that all the channels are subjected to Nakagami-m fading, we derive a closed-form expression of the exact secrecy outage probability (SOP). Furthermore, by exploiting the results about the approximations to the statistics of products of independent random variables, we provide approximate and asymptotic expressions of the SOP, and the latter enables us to obtain the secrecy diversity order (SDO), observe the impact of several parameters on the SOP, and provide a closed-form expression for the time switching (TS) ratio, which maximizes the secrecy throughput. Simulations present the accuracy of the derived SOP and SDO, the tightness of the approximation, and the effectiveness of the obtained TS ratio.

On the least common multiple of several random integers

Let Ln(k) denote the least common multiple of k independent random integers uniformly chosen in {1,2,…,n}. In this article, using a purely probabilistic approach, we derive a criterion for the convergence in distribution as n→∞ of f(Ln(k))nrk for a wide class of multiplicative arithmetic functions f with polynomial growth r∈R. Furthermore, we identify the limit as an infinite product of independent random variables indexed by the set of prime numbers. Along the way, we compute the generating function of a trimmed sum of independent geometric laws, occurring in the above infinite product. This generating function is rational; we relate it to the generating function of a certain max-type Diophantine equation, of which we solve a generalized version. Our results extend theorems by Erdős and Wintner (1939), Fernández and Fernández (2013) and Hilberdink and Tóth (2016).

Product of Three Random Variables and its Application in Relay Telecommunication Systems in the Presence of Multipath Fading

In this paper, the product of three random variables (RVs) will be considered. Distribution of the product of independent random variables is very important in many applied problems, including wireless relay telecommunication systems. A few of such products of three random variables are observed in this work: the level crossing rate (LCR) of the product of a Nakagami-m random variable, a Rician random variable and a Rayleigh random variable, and of the products of two Rician RVs and one Nakagami-m RV is calculated in closed forms and presented graphically. The LCR formula may be later used for derivation of average fade duration (AFD) of a wireless relay communication radio system with three sections, working in the multipath fading channel. The impact of fading parameters and multipath fading power on the LCR is analyzed based on the graphs presented.

Learning From Query-Answers

Tuple-independent and disjoint-independent probabilistic databases (TI- and DI-PDBs) represent uncertain data in a factorized form as a product of independent random variables that represent either tuples (TI-PDBs) or sets of tuples (DI-PDBs). When the user submits a query, the database derives the marginal probabilities of each output-tuple, exploiting the underlying assumptions of statistical independence. While query processing in TI- and DI-PDBs has been studied extensively, limited research has been dedicated to the problems of updating or deriving the parameters from observations of query results . Addressing this problem is the main focus of this article. We first introduce Beta Probabilistic Databases (B-PDBs), a generalization of TI-PDBs designed to support both (i) belief updating and (ii) parameter learning in a principled and scalable way. The key idea of B-PDBs is to treat each parameter as a latent, Beta-distributed random variable. We show how this simple expedient enables both belief updating and parameter learning in a principled way, without imposing any burden on regular query processing. Building on B-PDBs, we then introduce Dirichlet Probabilistic Databases (D-PDBs), a generalization of DI-PDBs with similar properties. We provide the following key contributions for both B- and D-PDBs: (i) We study the complexity of performing Bayesian belief updates and devise efficient algorithms for certain tractable classes of queries; (ii) we propose a soft-EM algorithm for computing maximum-likelihood estimates of the parameters; (iii) we present an algorithm for efficiently computing conditional probabilities, allowing us to efficiently implement B- and D-PDBs via a standard relational engine; and (iv) we support our conclusions with extensive experimental results.

A note on the product of independent random variables with regularly varying tails

The tail probability of the product of independent random variables is discussed. The class of random variables with regularly varying tails is known to be closed under multiplication and some explicit examples are given by Kifer-Varadhan [2]. We extend them.

An algebra of Stein operators

We build upon recent advances on the distributional aspect of Stein's method to propose a novel and flexible technique for computing Stein operators for random variables that can be written as products of independent random variables. We show that our results are valid for a wide class of distributions including normal, beta, variance-gamma, generalized gamma and many more. Our operators are kth degree differential operators with polynomial coefficients; they are straightforward to obtain even when the target density bears no explicit handle. As an application, we derive a new formula for the density of the product of k independent symmetric variance-gamma distributed random variables.

On computing distributions of products of random variables via Gaussian multiresolution analysis

Abstract We introduce a new approximate multiresolution analysis (MRA) using a single Gaussian as the scaling function, which we call Gaussian MRA (GMRA). As an initial application, we employ this new tool to accurately and efficiently compute the probability density function (PDF) of the product of independent random variables. In contrast with Monte-Carlo (MC) type methods (the only other universal approach known to address this problem), our method not only achieves accuracies beyond the reach of MC but also produces a PDF expressed as a Gaussian mixture, thus allowing for further efficient computations. We also show that an exact MRA corresponding to our GMRA can be constructed for a matching user-selected accuracy.

Non-uniform Estimates in the Approximation by the Irwin Law

We consider an approximation of a cumulative distribution function F(x) by the cumulative distributionfunction G(x) of the Irwin law. In this case, a function F(x) can be cumulative distribution functions of sums (products) ofindependent (dependent) random variables. Remainder term of the approximation is estimated by the cumulant method.The cumulant method is used by introducing special cumulants, satisfying the V. Statulevičius type condition. The mainresult is a nonuniform bound for the difference |F(x)-G(x)| in terms of special cumulants of the symmetric cumulativedistribution function F(x).

Non-abelian Littlewood–Offord inequalities

In 1943, Littlewood and Offord proved the first anti-concentration result for sums of independent random variables. Their result has since then been strengthened and generalized by generations of researchers, with applications in several areas of mathematics.In this paper, we present the first non-abelian analogue of the Littlewood–Offord result, a sharp anti-concentration inequality for products of independent random variables.

Wavelet estimation for derivative of a density in a GARCH-type model

We consider the GARCH-type model S = σ2Z where σ2 and Z are independent random variables. We assume that the density of σ2 is unknown with support [0, 1] but differentiable whereas the density fS of S is bounded. We will also assume that the probability density function of the random variable Z is known and has the same distribution as the ν-fold product of independent random variables uniformly distributed on the interval [0, 1]. We want to estimate the derivative of the density of σ2 from n independent and identically distributed observations of S. We will construct adaptive and non adaptive wavelet estimators for the derivative of the density and obtain sharp upper bounds on their mean integrated squared errors.

On some Integral Representations of Certain G-Functions

This is a brief exposition of some statistical techniques utilized to obtain several useful integral equations involving G-functions.

Two-sided bounds for [formula omitted]-norms of combinations of products of independent random variables

We show that for every positive p, the Lp-norm of linear combinations (with scalar or vector coefficients) of products of i.i.d. random variables, whose moduli have a nondegenerate distribution with the p-norm one, is comparable to the lp-norm of the coefficients and the constants are explicit. As a result the same holds for linear combinations of Riesz products.We also establish the upper and lower bounds of the Lp-moments of partial sums of perpetuities.

L 1-norm of combinations of products of independent random variables

We show that the L 1-norm of linear combinations (with scalar or vector coefficients) of products of i.i.d. nonnegative mean one random variables is comparable to the l 1-norm of the coefficients.

Hamburger moment problem for powers and products of random variables

We present new results on the Hamburger moment problem for probability distributions and apply them to characterize the moment determinacy of powers and products of i.i.d. random variables with values in the whole real line. Detailed proofs of all results are given followed by comments and examples. We also provide new and more transparent proofs of a few known results. E.g., we give a new and short proof that the product of three or more i.i.d. normal random variables is moment-indeterminate. The illustrations involve specific distributions such as the double generalized gamma (DGG), normal, Laplace and logistic. We show that sometimes, but not always, the power and the product of i.i.d. random variables (of the same odd ‘order’) share the same moment determinacy property. This is true for the DGG and the logistic distributions.The paper also treats two unconventional types of problems: products of independent random variables of different types and a random power of a given random variable. In particular, we show that the product of Laplace and logistic random variables, the product of logistic and exponential random variables, the product of normal and χ2 random variables, and the random power ZN, where Z~N and N is a Poisson random variable, are all moment-indeterminate.

On the limit distributions of some sums of a random multiplicative function

We study sums of a random multiplicative function; this is an example, of number-theoretic interest, of sums of products of independent random variables (chaoses). Using martingale methods, we establish a normal approximation for the sum over those n \leq x with k distinct prime factors, provided that k = o(log log x) as x \rightarrow \infty. We estimate the fourth moments of these sums, and use a conditioning argument to show that if k is of the order of magnitude of log log x then the analogous normal limit theorem does not hold. The methods extend to treat the sum over those n \leq x with at most k distinct prime factors, and in particular the sum over all n \leq x. We also treat a substantially generalised notion of random multiplicative function.

A Wiener-Hopf type factorization for the exponential functional of Lévy processes

For a Lévy process ξ=(ξt)t⩾0 drifting to −∞, we define the so-called exponential functional as follows: I ξ = ∫ 0 ∞ e ξ t d t . Under mild conditions on ξ, we show that the following factorization of exponential functionals: I ξ = d I H − × I Y holds, where × stands for the product of independent random variables, H− is the descending ladder height process of ξ and Y is a spectrally positive Lévy process with a negative mean constructed from its ascending ladder height process. As a by-product, we generate an integral or power series representation for the law of Iξ for a large class of Lévy processes with two-sided jumps and also derive some new distributional properties. The proof of our main result relies on a fine Markovian study of a class of generalized Ornstein–Uhlenbeck processes, which is itself of independent interest. We use and refine an alternative approach of studying the stationary measure of a Markov process which avoids some technicalities and difficulties that appear in the classical method of employing the generator of the dual Markov process.