随机解释变量的模型及其估计方法的研究

本文分析了随机性解释变量存在的可能性及其后果 ,其次对解释变量的随机性进行检验 ,最后讨论解释变量为随机变量的模型估计方法 ,包括正交回归估计法、正反向回归估计法、最小特征值估计法和工具变量估计法。

Complete Convergenceand Complete Moment Convergence for Weighted Sums of ANA Random Variables

In this paper,we investigate the complete convergence and complete moment conver-gence for weighted sums of arrays of rowwise asymptotically negatively associated(ANA)random variables,without assuming identical distribution.The obtained results not only extend those of An and Yuan[1]and Shen et al.[2]to the case of ANA random variables,but also partially improve them.

在役混凝土桥梁可靠度分析

桥梁结构在环境荷载与服役荷载的共同作用下,随着龄期的延长,出现耐久性能退化、承载能力降低的现象,利用可靠度方法研究了结构抗力的时变性能。通过建立荷载效应概率模型,实现对在役混凝土桥梁可靠度的分析。采用该方法对一座服役32年的钢筋混凝土简支T型梁桥进行了分析。结果表明,评估基准期内该桥梁结构的可靠指标的降低与抗弯抗力衰减规律一致,继续服役6年后结构的抗弯可靠指标不符合要求,需进行抗弯维修加固。

基于多权重混合分布模型的光伏出力波动特性研究

建立光伏发电理论出力模型,通过求取其实际出力和衰减理论出力差值提取光伏出力的随机性分量,采用不同单一概率密度函数对随机性分量进行拟合,并分析拟合结果存在较大偏差的原因。为降低拟合偏差,采用基于二分K均值聚类的权重值划分方法,提出三权重混合t Location-scale概率密度函数模型以描述光伏出力波动量的数字特征。最后通过Matlab仿真结果与指标值验证所提出模型拟合效果的优异性。

AN EFFICIENT SIMULATION OF MULTIPLE CORRELATED RAYLEIGH FADING ENVELOPES

In order to better assess the performance of wireless communication systems,it is desirable to produce multiple Rayleigh fading envelopes with specified correlations.In this paper,we analyze theoretically a procedure which generates correlated Gaussian random variables from independent Gaussian random variables and give a physical explanation for the limitation of this procedure.Then,based on some uncorrelated Rayleigh fading envelopes,a simple but efficient procedure for generating an arbitrary number of cross-correlated Rayleigh fading envelopes is proposed.Simulation results and computational complexity analysis are presented,which show that the proposed method has some advantages,such as high accuracy,low computational complexity and easy implementation,over the conventional simulation method.

Convergence Theorems for Fuzzy Superpramarts

The notions of fuzzy (super) pramart are introduced. Then the completeness and separability of metric space are discussed. A necessary and sufficient condition of convergence for fuzzy sequences is provided. Finally, the graph Kuratowski-Mosco convergence and D-convergence of fuzzy (super) pramart and quasi-martingale are studied.

A Strong Limit Theorem on Generalized Random Selection for m-valued Random Sequences

In this paper, a strong limit theorem on gambling strategy for binary Bernoulli sequence, (i.e.) irregularity theorem, is extended to random selection for dependent m-valued random variables, via using a new method-differentiability on net. Furthermore, by allowing the selection function to take value in finite interval [-M,M], the conception of random selection is generalized.

Benford＇s Law and Invariances

Benford＇s law is logarithmic law for distribution of leading digits formulated by P[D=d]= log（1＋1/d） where d is leading digit or group of digits. It＇s named by Frank Albert Benford （1938） who formulated mathematical model of this probability. Befbre him, the same observation was made by Simon Newcomb. This law has changed usual preasumption of equal probability of each digit on each position in number.The main characteristic properties of this law are base, scale, sum, inverse and product invariance. Base invariance means that logarithmic law is valid for any base. Inverse invariance means that logarithmic law for leading digits holds for inverse values in sample. Multiplication invariance means that if random variable X follows Benford＇s law and Y is arbitrary random variable with continuous density then XY follows Benford＇s law too. Sum invariance means that sums of significand are the same for any leading digit or group of digits. In this text method of testing sum invariance property is proposed.

Adaptive synchronization of a class of chaotic systems via variable structure control

The synchronization problem under two cases is considered. One is that the bound on the uncertainty existing in the controller is known, the other is that the bound is unknown. In the latter case, the simple adaptation laws for upper bound on the norm of the uncertainty is proposed. Using this adaptive upper bound, a variable structure control is designed. The proposed method does not guarantee the convergence of the adaptive upper bound to the real one but makes the system asymptotically stable.

Rosenthal's inequalities for independent and negatively dependent random variables under sub-linear expectations with applications

Classical Kolmogorov's and Rosenthal's inequalities for the maximum partial sums of random variables are basic tools for studying the strong laws of large numbers.In this paper,motived by the notion of independent and identically distributed random variables under the sub-linear expectation initiated by Peng(2008),we introduce the concept of negative dependence of random variables and establish Kolmogorov's and Rosenthal's inequalities for the maximum partial sums of negatively dependent random variables under the sub-linear expectations.As an application,we show that Kolmogorov's strong law of larger numbers holds for independent and identically distributed random variables under a continuous sub-linear expectation if and only if the corresponding Choquet integral is finite.

Sieve M-estimator for a semi-functional linear model

We propose sieve M-estimator for a semi-functional linear model in which the scalar response is explained by a linear operator of functional predictor and smooth functions of some real-valued random variables.Spline estimators of the functional coefficient and the smooth functions are considered,and by selecting appropriate knot numbers the optimal convergence rate and the asymptotic normality can be obtained under some mild conditions.Some simulation results and a real data example are presented to illustrate the performance of our estimation method.

Moment bounds for IID sequences under sublinear expectations

With the notion of independent identically distributed(IID) random variables under sublinear expectations introduced by Peng,we investigate moment bounds for IID sequences under sublinear expectations. We obtain a moment inequality for a sequence of IID random variables under sublinear expectations. As an application of this inequality,we get the following result:For any continuous functionsatisfying the growth condition |(x) | C(1 + |x|p) for some C > 0,p 1 depending on ,the central limit theorem under sublinear expectations obtained by Peng still holds.

Response-surface-based structural reliability analysis with random and interval mixed uncertainties

Traditional reliability analysis requires probability distributions of all the uncertain parameters.However,in many practical applications,the variation bounds can be only determined for the parameters with limited information.A complex hybrid reliability problem then will be caused when the random and interval variables coexist in a same structure.In this paper,by introducing the response surface technique,we develop a new hybrid reliability method to efficiently compute the interval of the failure probability of the structure due to the probability-interval hybrid uncertainty.The present method consists of a sequence of iterations.At each step,a response surface model is constructed for the limit-state function by using a quadratic polynomial and a modified axial experimental design method.An approximate hybrid reliability problem is created based on the response surface model,which is subsequently solved by an efficient decoupling approach.An updating strategy is suggested to improve the quality of the response surface and whereby ensure the reliability analysis precision.A computational procedure is then summarized for the whole iterations.Four numerical examples and also a practical application are provided to demonstrate the effectiveness of the present method.

Strong Convergence for Weighted Sums of Negatively Associated Arrays

Let {Xni} be an array of rowwise negatively associated random variables and Tnk=k∑i=1 i^a Xni for a ≥ -1, Snk =∑｜i｜≤k Ф（i/nη）1/nη Xni for η∈（0,1],where Ф is some function. The author studies necessary and sufficient conditions of ∞∑n=1 AnP（max 1≤k≤n｜Tnk｜〉εBn）〈∞ and ∞∑n=1 CnP（max 0≤k≤mn｜Snk｜〉εDn）〈∞ for all ε 〉 0, where An, Bn, Cn and Dn are some positive constants, mn ∈ N with mn /nη →∞. The results of Lanzinger and Stadtmfiller in 2003 are extended from the i.i.d, case to the case of the negatively associated, not necessarily identically distributed random variables. Also, the result of Pruss in 2003 on independent variables reduces to a special case of the present paper; furthermore, the necessity part of his result is complemented.

Consistency of Chi-Squared Test with Varying Number of Classes

The classical chi-squared goodness of fit test assumes the number of classes is fixed,meanwhile the test statistic has a limiting chi-square distribution under the null hypothesis.It is well known that the number of classes varying with sample size in the test has attached more and more attention.However,in this situation,there is not theoretical results for the asymptotic property of such chi-squared test statistic.This paper proves the consistency of chi-squared test with varying number of classes under some conditions.Meanwhile,the authors also give a convergence rate of KolmogorovSimirnov distance between the test statistic and corresponding chi-square distributed random variable.In addition,a real example and simulation results validate the reasonability of theoretical result and the superiority of chi-squared test with varying number of classes.

An L_2-theory for a class of SPDEs driven by Lévy processes

In this paper we present an L2-theory for a class of stochastic partial differential equations driven by Levy processes. The coefficients of the equations are random functions depending on time and space variables, and no smoothness assumption of the coefficients is assumed.

A collocation reliability analysis method for probabilistic and fuzzy mixed variables

The main bottleneck of the reliability analysis of structures with aleatory and epistemic uncertainties is the contradiction between the accuracy requirement and computational efforts.Existing methods are either computationally unaffordable or with limited application scope.Accordingly,a new technique for capturing the minimal and maximal point vectors instead of the extremum of the function is developed and thus a novel reliability analysis method for probabilistic and fuzzy mixed variables is proposed.The fuzziness propagation in the random reliability,which is the focus of the present study,is performed by the proposed method,in which the minimal and maximal point vectors of the structural random reliability with respect to fuzzy variables are calculated dimension by dimension based on the Chebyshev orthogonal polynomial approximation.First-Order,Second-Moment(FOSM)method which can be replaced by its counterparts is utilized to calculate the structural random reliability.Both the accuracy and efficiency of the proposed method are validated by numerical examples and engineering applications introduced in the paper.

THE LIMIT THEOREM FOR DEPENDENT RANDOM VARIABLES WITH APPLICATIONS TO AUTOREGRESSION MODELS

This paper studies the autoregression models of order one, in a general time series setting that allows for weakly dependent innovations. Let {Xt} be a linear process defined by Xt =∑k=0^∞ψ kεt-k, where {ψk, k ≥ 0} is a sequence of real numbers and {εk, k = 0, ±1, ±2,...} is a sequence of random variables. Two results are proved in this paper. In the first result, assuming that {εk, k ≥ 1} is a sequence of asymptotically linear negative quadrant dependent （ALNQD） random variables, the authors find the limiting distributions of the least squares estimator and the associated regression t statistic. It is interesting that the limiting distributions are similar to the one found in earlier work under the assumption of i.i.d, innovations. In the second result the authors prove that the least squares estimator is not a strong consistency estimator of the autoregressive parameter a when {εk, k ≥ 1} is a sequence of negatively associated （NA） random variables, and ψ0 = 1, ψk = 0, k ≥ 1.