Euler-Maruyama approximations for SDEs with non-Lipschitz coefficients and applications

In this paper Euler-Maruyama approximation for SDE with non-Lipschitz coefficients is proved to converge uniformly to the solution in Lp-space with respect to the time and starting points. As an application, we also study the existence of solution and large deviation principle for anticipative SDE with random initial condition.     

An L 2-theory for a class of SPDEs driven by Lévy processes

In this paper we present an L 2-theory for a class of stochastic partial differential equations driven by Lévy processes.The coefficients of the equations are random functions depending on time and space variables,and no smoothness assumption of the coefficients is assumed.     

Limit theory for random coefficient first-order autoregressive process under martingale difference error sequence

Phillips and Magdalinos (2007) [1] gave the asymptotic theory for autoregressive time series with a root of the form ρ_n=1+c/k_n, where k_n is a deterministic sequence. In this paper, an extension to the more general case where the coefficients of an AR(1) model is a random variable and the error sequence is a sequence of martingale differences is discussed. A conditional least squares estimator of the autoregressive coefficient is derived and shown to be asymptotically normal. This extends the result of Phillips and Magdalinos (2007) [1] for stationary and near-stationary cases.     

Zeros of a random algebraic polynomial with coefficient means in geometric progression

This paper provides the mathematical expectation for the number of real zeros of an algebraic polynomial with non-identical random coefficients. We assume that the coefficients {a_j}ⁿ⁻¹_j=0 of the polynomial T(x)=a₀+a₁x+a₂x²+?+a_n−1xⁿ⁻¹ are normally distributed, with mean E(a_j)=μ^j+1, where μ≠0, and constant non-zero variance. It is shown that the behaviour of the random polynomial is independent of the variance on the interval (−1,1); it differs, however, for the cases of |μ|<1 and |μ|>1. On the intervals (−∞,−1) and (1,∞) we find the expected number of real zeros is governed by an interesting relationship between the means of the coefficients and their common variance. Our result is consistent with those of previous works for identically distributed coefficients, in that the expected number of real zeros for μ≠0 is half of that for μ=0.     

Stability of the Kolmogorov flow and its modifications

Recurrence formulas are obtained for the kth term of the long wavelength asymptotics in the stability problem for general two-dimensional viscous incompressible shear flows. It is shown that the eigenvalues of the linear eigenvalue problem are odd functions of the wave number, while the critical values of viscosity are even functions. If the velocity averaged over the long period is nonzero, then the loss of stability is oscillatory. If the averaged velocity is zero, then the loss of stability can be monotone or oscillatory. If the deviation of the velocity from its period-average value is an odd function of spatial variable about some x ₀, then the expansion coefficients of the velocity perturbations are even functions about x ₀ for even powers of the wave number and odd functions about for x ₀ odd powers of the wave number, while the expansion coefficients of the pressure perturbations have an opposite property. In this case, the eigenvalues can be found precisely. As a result, the monotone loss of stability in the Kolmogorov flow can be substantiated by a method other than those available in the literature.     

Bargaining set theory and stability in coalition governments

Various bargaining set theories are compared as predictors of coalition government portfolio distribution. While the kernel and B₁-bargaining set are known to exist in voting games with side payments, it is argued that the kernel, and thus B₁, are poor predictors. The B₂-bargaining set, a subset of B₁, when it exists is shown to be a good payoff predictor in a fractionalized and depolarized parliamentary situation (Finland: 1945ndash;1971). Moreover this predictor provides some explanation for the formation of surplus (winning but not minimal) coalitions.     

Sharp Estimates for the Coefficients of the Inverse Functions of the Nevanlinna Univalent Functions of the Classes N1 and N2

In the paper the extremum of a typical functional in the class N₂ is found. In particular, it is shown its application for determination of the extremums of the coefficients of the inverse functions of the Nevanlinna univalent functions of the class N₂. A conjecture for these coefficients is stated.     

Limit theorems for a supercritical branching process with immigration in a random environment

Let (Z_n) be a supercritical branching process with immigration in a random environment. Firstly, we prove that under a simple log moment condition on the offspring and immigration distributions, the naturally normalized population size W_n converges almost surely to a finite random variable W. Secondly, we show criterions for the non-degeneracy and for the existence of moments of the limit random variable W. Finally, we establish a central limit theorem, a large deviation principle and a moderate deviation principle about log Z_n.     

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.     

Martin boundary of a reflected random walk on a half-space

The complete representation of the Martin compactification for reflected random walks on a half-space ${\mathbb{Z}^d\times\mathbb{N}}$ is obtained. It is shown that the full Martin compactification is in general not homeomorphic to the “radial” compactification obtained by Ney and Spitzer for the homogeneous random walks in ${\mathbb{Z}^d}$ : convergence of a sequence of points ${z_n\in\mathbb{Z}^{d-1}\times\mathbb{N}}$ to a point of on the Martin boundary does not imply convergence of the sequence z _n /|z _n | on the unit sphere S ^d . Our approach relies on the large deviation properties of the scaled processes and uses Pascal’s method combined with the ratio limit theorem. The existence of non-radial limits is related to non-linear optimal large deviation trajectories.     

Analyzing Ek|Er|c queues

In this paper we study a system consisting of c parallel identical servers and a common queue. The service times are Erlang-r distributed and the interarrival times are Erlang-k distributed. The service discipline is first-come first-served. The waiting process may be characterized by (n₋₁, n₀, n₁,…, n_c) where n₋₁ represents the number of remaining arrival stages, n₀ the number of waiting jobs and n_i, i = 1,…, c, the number of remaining service stages for server i. Bertsimas has proved that the equilibrium probability for saturated states (i.e. states with all servers busy) can be written as a linear combination of geometric terms with n₀ as exponent. In the present paper it is shown that the coefficients also have a geometric form with respect to n₋₁, n₁, …, n_c. It is also shown how the factors may be found efficiently. The present paper uses a direct approach for solving the equilibrium equations rather than a generating function approach as Bertsimas does. The direct approach is based on separation of variables and has been inspired by previous work of two of the authors on the shortest queue problem in particular and the two-dimensional random walk more generally. The characterization of the equilibrium probabilities leads to exact expressions for performance measures such as the moments of the queue length and the waiting time, which are useful for numerical computations. Numerical results are presented.     

Some congruence properties of three well-known sequences: Two notes

The three sequences mentioned in the title are Ramanujan's τ-function, the coefficients c_n of Klein, Fricke, and Shimura, and the sequence a_n of Apèry numbers. In the first note, it is shown that c_n ≡ τ(n)(mod 11). In the second note it is shown that for a prime p, a_p+1 ≡ 25 + 60p(mod p²).     

Random matrix models of stochastic integral type for free infinitely divisible distributions

The Bercovici-Pata bijection maps the set of classical infinitely divisible distributions to the set of free infinitely divisible distributions. The purpose of this work is to study random matrix models for free infinitely divisible distributions under this bijection. First, we find a specific form of the polar decomposition for the Lévy measures of the random matrix models considered in Benaych-Georges [6] who introduced the models through their laws. Second, random matrix models for free infinitely divisible distributions are built consisting of infinitely divisible matrix stochastic integrals whenever their corresponding classical infinitely divisible distributions admit stochastic integral representations. These random matrix models are realizations of random matrices given by stochastic integrals with respect to matrix-valued Lévy processes. Examples of these random matrix models for several classes of free infinitely divisible distributions are given. In particular, it is shown that any free selfdecomposable infinitely divisible distribution has a random matrix model of Ornstein-Uhlenbeck type ?? ₀ ^?? e ^?1 d?? _t ^d , d ?? 1, where ?? _t ^d is a d × d matrix-valued Lévy process satisfying an I _log condition.     

On unimodal sequences of graphical invariants

Let b_i denote the number of ways to select a subset of i independent edges in a given graph. It is shown that the sequence of b_i's is unimodal, that is, there exists an r such that b₀ < b₁ < … < b_r ≥ b_r+1 > … > b_m. Similarly, for any bigraph, the nonzero coefficients in the characteristic polynomial are shown to be unimodal in magnitude. Finally, it is suggested that the approach used here might be applied to verify the conjecture that the coefficients in the chromatic polynomial are unimodal in magnitude.     

Easy distributions for combinatorial optimization problems with probabilistic constraints

We show how we can linearize individual probabilistic linear constraints with binary variables when all coefficients are independently distributed according to either N(μ_i,λμ_i), for some λ>0 and μ_i>0, or Γ(k_i,θ) for some θ>0 and k_i>0. The constraint can also be linearized when the coefficients are independent and identically distributed and either positive or strictly stable random variables.     

Zolotarev problem in the metric of L1([−1, 1])

In this paper we solve the problem of the determination of a polynomial of degree n with given two leading coefficients which has the least deviation from zero in the metric of L₁ ([?1, 1]). The extremal polynomial is expressed in the form of some linear combination of Chebyshev polynomials of the second kind.     

On a continuous analogue of the stochastic difference equation Xn=ρXn-1+Bn

Let B₁, B₂, ... be a sequence of independent, identically distributed random variables, letX₀ be a random variable that is independent ofB_n forn?1, let ρ be a constant such that 0<ρ<1 and letX₁,X₂, ... be another sequence of random variables that are defined recursively by the relationshipsX_n=ρX_n-1+B_n. It can be shown that the sequence of random variablesX₁,X₂, ... converges in law to a random variableX if and only ifE[log⁺¦B₁¦]<∞. In this paper we let {B(t):0≦t<∞} be a stochastic process with independent, homogeneous increments and define another stochastic process {X(t):0?t<∞} that stands in the same relationship to the stochastic process {B(t):0?t<∞} as the sequence of random variablesX₁,X₂,...stands toB₁,B₂,.... It is shown thatX(t) converges in law to a random variableX ast →+∞ if and only ifE[log⁺¦B(1)¦]<∞ in which caseX has a distribution function of class L. Several other related results are obtained. The main analytical tool used to obtain these results is a theorem of Lukacs concerning characteristic functions of certain stochastic integrals.     

David G. Luenberger,Linear and nonlinear programming, 2nd edition,Addison-Wesley,Reading (1984), xv + 492 pages, $35.50

This paper considers a stochastic version of the linear continuous type knapsack problem in which the cost coefficients are random variables. The problem is to find an optimal solution and an optimal probability level of the chance constraint. This problem P₀ is first transformed into a deterministic equivalent problem P. Then a subproblem with a positive parameter is introduced and a close relation between P and its subproblem is shown. Further, an auxiliary problem of the subproblem is introduced and a direct relation between P and the auxiliary problem is derived through a relation connecting the subproblem and its auxiliary problem. Fully utilizing these relations, an efficient algorithm is proposed that finds an optimal solution of P in at most O(n⁴) computational time where n is the number of decision variables. Finally, further research problems are discussed.     

Maximizing the probability of correctly ordering random variables using linear predictors

Let (T₁, x₁), (T₂, x₂), …, (T_n, x_n) be a sample from a multivariate normal distribution where T_i are (unobservable) random variables and x_i are random vectors in R^k. If the sample is either independent and identically distributed or satisfies a multivariate components of variance model, then the probability of correctly ordering {T_i} is maximized by ranking according to the order of the best linear predictors {E(T_i|x_i)}. Furthermore, it orderings are chosen according to linear functions {b′x_i} then the conditional probability of correct order given (T_i = t₁; i = 1, …, n) is maximized when b′x_i is the best linear predictor. Examples are given to show that linear predictors may not be optimal and that using a linear combination other that the best linear predictor may give a greater probability of correctly ordering {T_i} if {(T_i, x_i)} are independent but not identically distributed, or if the distributions are not normal.