一阶中立型微分差分方程振动性的充分必要条件

证明了一类具有周期系数的中立型微分差分方程振动的充分必要条件是其相应的特征方程无实根 .     

Distributionally robust inference for extreme Value-at-Risk

Under general multivariate regular variation conditions, the extreme Value-at-Risk of a portfolio can be expressed as an integral of a known kernel with respect to a generally unknown spectral measure supported on the unit simplex. The estimation of the spectral measure is challenging in practice and virtually impossible in high dimensions. This motivates the problem studied in this work, which is to find universal lower and upper bounds of the extreme Value-at-Risk under practically estimable constraints. That is, we study the infimum and supremum of the extreme Value-at-Risk functional, over the infinite dimensional space of all possible spectral measures that meet a finite set of constraints. We focus on extremal coefficient constraints, which are popular and easy to interpret in practice. Our contributions are twofold. First, we show that optimization problems over an infinite dimensional space of spectral measures are in fact dual problems to linear semi-infinite programs (LSIPs) – linear optimization problems in Euclidean space with an uncountable set of linear constraints. This allows us to prove that the optimal solutions are in fact attained by discrete spectral measures supported on finitely many atoms. Second, in the case of balanced portfolia, we establish further structural results for the lower bounds as well as closed form solutions for both the lower- and upper-bounds of extreme Value-at-Risk in the special case of a single extremal coefficient constraint. The solutions unveil important connections to the Tawn–Molchanov max-stable models. The results are illustrated with two applications: a real data example and closed-form formulae in a market plus sectors framework.     

Scattering states of Woods–Saxon interaction in minimal length quantum mechanics

We propose a very simple approach to deal with the problems of the modified Schrödinger equation due to minimal length and thereby solve the minimal length Schrödinger equation in the presence of a non-minimal Woods–Saxon interaction. The transmission and reflection coefficients are reported as well.     

Bayesian‐type count data models with varying coefficients: estimation and testing in the presence of overdispersion

In this paper we study varying‐coefficient models for count data. A Bayesian approach is taken to model the variability of the regression parameters. Based on a Kalman filter procedure the varying coefficients are estimated as the mode of the posterior distribution. All hyperparameters, including an overdispersion parameter in the negative binomial varying‐coefficient model (NBVC), are estimated as ML‐estimators using an EM‐type algorithm. A bootstrapping test of the fixed‐coefficient hypothesis against a varying‐coefficient alternative is proposed, which is evaluated running a simulation study. The study shows that the choice of a suitable count data model is of special importance in the framework of varying‐coefficient models. The methodology is illustrated analysing the determinants of the number of individual doctor visits. Copyright © 2001 John Wiley & Sons, Ltd.     

Application of Kirkwood-Buff theory to electrolyte solutions. III. Activity coefficients of aqueous NaCl between 273.15 and 573.15 K

Using a theory recently developed for the interpretation of activity coefficients of 1:1 electrolytes up to high concentrations in aqueous solution at 25°C, we have analysed available data for aqueous sodium chloride solutions up to saturation in the temperature range 273.15–573.15 K. The approach, which is based on Kirkwood-Buff theory and uses the truncated Poisson-Boltzmann equation to obtain the required information about the various ion-ion radial distributions, is able to fit the results to high accuracy with minimum of parameters, viz, three, of which one is the distance of closest approach, the other two relate to ion-solvent interactions and/or higher order terms in the ion-ion interaction.     

Three-Step Predictor-Corrector of Exponential Fitting Method for Nonlinear Schroedinger Equations

We develop the three-step explicit and implicit schemes of exponential fitting methods. We use the three- step explicit exponential fitting scheme to predict an approximation, then use the three-step implicit exponential fitting scheme to correct this prediction. This combination is called the three-step predictor-corrector of exponential fitting method. The three-step predictor-corrector of exponential fitting method is applied to numerically compute the coupled nonlinear Schroedinger equation and the nonlinear Schroedinger equation with varying coefficients. The numerical results show that the scheme is highly accurate.     

Complete study on a bi-center problem for the <Emphasis Type="Italic">Z</Emphasis><Subscript>2</Subscript>-equivariant cubic vector fields

For the planar Z ₂-equivariant cubic systems having two elementary focuses, the characterization of a bi-center problem and shortened expressions of the first six Liapunov constants are completely discussed. The necessary and sufficient conditions for the existence of the bi-center are obtained. All possible first integrals are given. Under small Z ₂-equivariant cubic perturbations, the conclusion that there exist at most 12 small-amplitude limit cycles with the scheme 〈6 ∐ 6〉 is proved.