Stratified Monte Carlo Quadrature for Continuous Random Fields

We consider the problem of numerical approximation of integrals of random fields over a unit hypercube. We use a stratified Monte Carlo quadrature and measure the approximation performance by the mean squared error. The quadrature is defined by a finite number of stratified randomly chosen observations with the partition generated by a rectangular grid (or design). We study the class of locally stationary random fields whose local behaviour is like a fractional Brownian field in the mean square sense and find the asymptotic approximation accuracy for a sequence of designs for large number of the observations. For the Hölder class of random functions, we provide an upper bound for the approximation error. Additionally, for a certain class of isotropic random functions with an isolated singularity at the origin, we construct a sequence of designs eliminating the effect of the singularity point.     

Qualitative analysis on a diffusive SIS epidemic system with logistic source and spontaneous infection in a heterogeneous environment

In this paper, we consider an SIS epidemic reaction–diffusion model with spontaneous infection and logistic source in a heterogeneous environment. The uniform bounds of solutions are established, and the global asymptotic stability of the constant endemic equilibrium is discussed in the case of homogeneous environment. This paper aims to analyze the asymptotic profile of endemic equilibria (when it exists) as the diffusion rate of the susceptible or infected population is small or large. Our results on this new model reveal that varying total population and spontaneous infection can enhance persistence of infectious disease, which may provide some implications on disease control and prediction.     

Superinterpolation in Highly Oscillatory Quadrature

Asymptotic expansions for oscillatory integrals typically depend on the values and derivatives of the integrand at a small number of critical points. We show that using values of the integrand at certain complex points close to the critical points can actually yield a higher asymptotic order approximation to the integral. This superinterpolation property has interesting ramifications for numerical methods based on exploiting asymptotic behaviour. The asymptotic convergence rates of Filon-type methods can be doubled at no additional cost. Numerical steepest descent methods already exhibit this high asymptotic order, but their analyticity requirements can be significantly relaxed. The method can be applied to general oscillators with stationary points as well, through a simple change of variables.     

Bayes factors in the presence of population stratification

Hidden population stratification (PS) is a main concern in the analysis of case-control genetic association studies. All methods to correct for hidden PS have been focused on classical hypothesis testing, and cannot be directly applied to Bayesian analysis. In this paper, to study the impact and the correction of hidden PS on Bayes factor (BF), we use a simple approximation of BF in terms of the maximum likelihood estimates of the odds ratio (OR) and its asymptotic variance. One advantage is that the commonly used principal components analysis method with a large panel of null markers scanned from existing genome-wide association studies can be directly employed to correct for hidden PS in estimating the OR and its asymptotic variance, through which a correction to BF for hidden PS can be achieved. Using simulations, we examine the impact of ignoring hidden PS on BF and show that the proposed method yields an appropriate correction in Bayesian analysis.     

A nonparametric version of Wilks’ lambda—Asymptotic results and small sample approximations

We propose a nonparametric version of Wilks’ lambda (the multivariate likelihood ratio test) and investigate its asymptotic properties under the two different scenarios of either large sample size or large number of samples. For unbalanced samples, a weighted and an unweighted variant are introduced. The unweighted variant of the proposed test appears to be novel also in the normal-theory context.The theoretical results are supplemented by a simulation study with parameter settings that are motivated by clinical and agricultural data, considering in particular the performance for small sample sizes, small number of samples, and varying dimensions. Inference methods based on the asymptotic sampling distribution and a small sample approximation are compared to permutation tests and to other parametric and nonparametric procedures. Application of the proposed method is illustrated by examples.     

Qualitative analysis on an SIRS reaction–diffusion epidemic model with saturation infection mechanism

In this paper, we deal with an SIRS reaction–diffusion epidemic model with saturation infection mechanism. Based on the uniform boundedness of the parabolic system, we investigate the extinction and persistence of the infectious disease in terms of the basic reproduction number. To better investigate the effects of infection mechanism and individual diffusion, we further analyze the asymptotic profiles of the endemic equilibrium for small or large motility rate and large saturation rate. In particular it is shown that large saturation may cause the elimination of disease. Our study may provide some significant useful insight on disease control and prevention.     

On the asymptotic behaviour of some new gradient methods

The Barzilai-Borwein (BB) gradient method, and some other new gradient methods have shown themselves to be competitive with conjugate gradient methods for solving large dimension nonlinear unconstrained optimization problems. Little is known about the asymptotic behaviour, even when applied to n−dimensional quadratic functions, except in the case that n=2. We show in the quadratic case how it is possible to compute this asymptotic behaviour, and observe that as n increases there is a transition from superlinear to linear convergence at some value of n≥4, depending on the method. By neglecting certain terms in the recurrence relations we define simplified versions of the methods, which are able to predict this transition. The simplified methods also predict that for larger values of n, the eigencomponents of the gradient vectors converge in modulus to a common value, which is a similar to a property observed to hold in the real methods. Some unusual and interesting recurrence relations are analysed in the course of the study.This work was supported by the EPRSC in UK (no. GR/R87208/01) and the Chinese NSF grant (no. 10171104)     

On certain problems in the analytical arithmetic of quadratic forms arising from the theory of curves of genus 2 with elliptic differentials

For an imaginary quadratic fieldK we study the asymptotic behaviour (with respect top) of the number of integers inK with norm of the formk(p−k) for some 1≤k≤p−1, wherep is a prime number. The motivation for studying this problem is that it is known by recent results due to G. Frey and E. Kani that knowledge of this asymptotic behaviour can lead to statements of existence of curves of genus 2 with elliptic differentials in particular cases. We give a general, and from one point of view complete, answer to this question on asymptotic behaviour. This answer is derived from a theorem concerning the number of representations of a natural number by certain quaternary quadratic forms. This second result may be of some independent interest because it can be seen as a generalisation of the classical theorem of Jacobi on the number of representations of a natural number as a sum of 4 squares.     

Asymptotic relations in queueing theory

For the GI?G?1 queueing system a number of asymptotic results are reviewed. Discussed are asymptotics related to the time parameter for t → ∞ relaxation times, heavy traffic theory, restricted accessibility with large bounds, approximation by diffusion processes, exponential and regular variation of the tail of the waiting time distribution, limit theorems and extreme value theorems.     

Asymptotic formulas for g-gonal sequence factorials

In this note, we provide basic asymptotic formulas for approximating large g-gonal sequence factorials by using Stirling and Burnside asymptotic approximation formulas for large factorials. More accurate asymptotic approximation formulas for large g-gonal sequence factorials resulting from some recent, more accurate asymptotic formulas for large factorials that have appeared in the literature are presented.     

More Accurate Skin-Depth Approximations

The penetration of a high-frequency alternating or rotatingmagnetic field into a conductor is considered. The magneticReynolds number is assumed to be small. The standard high-frequency(or skin-depth) approximation is shown to be incorrect in theinterior of the conductor, leading to large relative errors.Two terms of the correct expansion are calculated. It is foundthat a ‘focusing’ effect occurs, in a manner similarto the formation of caustics in geometrical optics. As a result,at some interior points the field is several times larger thanexpected. The behaviour near such singular points is found.The implications for electromagnetically driven fluid flow arebriefly discussed.     

Asymptotic Expansion for the Null Distribution of the <Emphasis Type="Italic">F</Emphasis>-statistic in One-way ANOVA under Non-normality

In this paper we derive the asymptotic expansion of the null distribution of the F-statistic in one-way ANOVA under non-normality. The asymptotic framework is when the number of treatments is moderate but sample size per treatment (replication size) is small. This kind of asymptotics will be relevant, for example, to agricultural screening trials where large number of cultivars are compared with few replications per cultivar. There is also a huge potential for the application of this kind of asymptotics in microarray experiments. Based on the asymptotic expansion we will devise a transformation that speeds up the convergence to the limiting distribution. The results indicate that the approximation based on limiting distribution are unsatisfactory unless number of treatments is very large. Our numerical investigations reveal that our asymptotic expansion performs better than other methods in the literature when there is skewness in the data or even when the data comes from a symmetric distribution with heavy tails.     

迁移方程的扩散近似的收敛性

导出了迁移方程的扩散近似方程．说明了它的离散纵标方法在区间内和边界上都有扩散极限，它的解关于一致地收敛于迁移方程的解．其收敛性的证明是依据其渐近扩散展开式，在边界层上得到的误差估计逼近其离散纵标方法的解．     

Qualitative properties of a class of Fleming-Viot operators

We indicate some qualitative properties of Fleming--Viot second order differential operators on the d-dimensional simplex, such as an inductive characterization of its domain and some spectral properties connected with the asymptotic behavior of the generated semigroup. These properties turn out to be very useful in the approximation of the solution of the evolution problem associated with Fleming--Viot operators, which are very important as diffusion models in population genetics.     

Empirical Best Prediction for Small Area Inference with Binary Data

The paper introduces a frequentist's alternative to the recently developed hierarchical Bayes methods for small area estimation with binary data. Specifically, the best predictor (BP) and empirical best predictor (EBP) of small area specific random effect are developed in the context of a mixed logistic model and different asymptotic properties of the proposed BP and EBP are studied. An approximation to the mean squared error (MSE) of the proposed EBP correct up to the order o(m ^–1) is obtained, where m denotes the number of small areas. The asymptotic behavior of the relative savings loss (RSL) demonstrates the superiority of the proposed EBP over the usual small area proportion.     

Approximation of Multivariate Functions with a Certain Mixed Smoothness by Entire Functions

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High‐order asymptotic‐preserving schemes for linear systems: Application to the Goldstein–Taylor equations

In this work, we address the numerical approximation of linear systems with possibly stiff source terms which induce an asymptotic diffusion limit. More precisely, we are interested in the design of high‐order asymptotic‐preserving schemes. Our approach is based on a very simple modification of the numerical flux associated with the usual HLL scheme. This alteration can be understood as a numerical diffusion reduction technique and allows to capture the correct asymptotic behavior in the diffusion limit and to consider uniformly high‐order extensions. We more specifically consider the case of the Goldstein–Taylor model but the overall approach is shown to be easily adapted to more general systems.     

On the asymptotics of locally dependent point processes

We investigate a family of approximating processes that can capture the asymptotic behaviour of locally dependent point processes. We prove two theorems presented to accommodate respectively the positively and negatively related dependent structures. Three examples are given to illustrate that our approximating processes can circumvent the technical difficulties encountered in compound Poisson process approximation (see Barbour and Månsson (2002) [10]) and our approximation error bound decreases when the mean number of the random events increases, in contrast to the increasing of bounds for compound Poisson process approximation.     

Diffusion approximation of branching migration processes

We study the behavior of a Galton-Watson process with homogeneous migration component stopped at zero (i.e., the state zero is absorbing). Assuming that the process is initiated at time zero by a large number of particles, we find a diffusion approximation for this process in the case where the average number of offspring per individual is close to one. Supported by the Russian Foundation for Fundamental Research (grant Nos. 96-01-00338 and 96-15-96092) and INTAS-RFBR (grant No. 95-0099). Proceedings of the Seminar on Stability Problems for Stochastic Models, Hajdúszoboszló, Hungary, 1997, Part III.