相对强弱指数的最佳参数组合

本文首先提出了广义均匀设计抽样,并给出了利用广义均匀设计抽样在半连续半离散区域上求取函数最值的方法,之后利用这种方法,针对香港股票市场,给出了技术分析指标－相对强弱指数的最佳参数组合．     

随机化均匀设计及其在股票交易上的应用

本文首先利用Bundschuh和朱尧辰（1993）的偏差计算公式,给出了构造某些非平衡均匀设计方法及相应的均匀设计表,之后把随机化均匀设计与移动平均线相结合,给出了移动平均线的最佳参数组合。     

低偏差OALHD的构造

本文给出了利用均匀设计和正交表构造低偏差OALH设计的方法，该方法构造的设计既有优良的均匀性具有正交设计的均衡性，一个更重要的优点是可以构造较大样本容量的设计点集，本文同时给出了某些参数的均匀设计表，这些设计优于现有的均匀设计，具有实用价值。     

均匀设计抽样的一、二阶矩

（７）中给出并研究了均匀设计抽样（ＵＤＳ）及随机化均匀设计（ＲＵＤ）的一些优良性质，作者给出该设计和抽样的一、二阶矩。     

均匀设计抽样在股市投资决策上的应用

本文首先给出了在区域D(b)=｛（x,y):0≤y≤x≤b｝上的均匀抽样,以及利用均匀设计抽样求取函数在D的最大值的序贯方法,最后把此方法应用于股市的技术分析中,即把此序贯方法与股票的动态分析相结合,给出了在香港股市应用动态分析的最佳参数。     

正交表型均匀LH设计和抽样

本文提出了一种新的设计和抽样方法－正交表型均匀LH设计和抽样,证明了这种抽样空间是OALH抽样空间的优良子集。这种设计和抽样空间中所有样本都与初始设计具有同阶低偏差等一些优良性质。并将它用于数值积分,证明了对有关参数的估计的方差阶低于其他抽样。同时还给出了有关的模拟结果。     

均匀试验设计的理论、方法和应用——历史回顾

本文回顾计算机仿真试验设计的主要两种方法:拉丁超立体抽样和均匀设计,在过去二十五年的发展,特别是均匀设计的发展,包括均匀设计的优良性研究、新的均匀性测度、均匀设计表的构造,以及均匀性在因子设计中的应用。     

均匀设计及其应用（Ⅲ）

均匀设计及其应用（Ⅲ）方开泰第三章均匀设计表的构造本章介绍均匀设计表的构造和使用表的来源，其中涉及到均匀度的度量。在理解了表的构造原理冬我们就能比较灵活地运用均匀设计，故本章也介绍均匀设计这方面的知识。§１均匀设计表的构造定义１：每一个均匀设计表是一...     

均匀设计抽样及其优良性质

抽样和设计是计算机试验的一个重要研究课题。本文提出了一种新的抽样方法—均匀设计抽样,研究了它的一些基本性质,并将其应用于数值积分近似计算,这种抽样是王元和方开泰(1981)均匀设计思想的一个发展,也是对Latin Hypercube抽样的一个重要改进,通过与Monte Carlo方法,Latin Hypercube抽样(包括OA-Based Latin Hypercube抽样)和均匀设计的比较,表明了这种抽样的优越性,最后还讨论了在一般分布情形下如何应用这种抽样。     

均匀设计抽样的偏差

该文证明了均匀设计抽样点集的偏差为O（n－1（lnn）d），另外也证明了随机化均匀设计点集的偏差有同样的阶．最后进行了随机模拟．     

从设计到抽样

张润楚,王兆军（１９９６）提出了均匀设计抽样,将均匀设计变成抽样．本文给出一种由设计到抽样的一般方法,它可以将任何一个有优良均匀性的设计点集变成所有样本都有同样优良均匀性的抽样。     

混水平均匀设计的构造

我们用离散偏差来度量部分因子设计的均匀性,本文的目的在于寻找一些构造混水平均匀设计的方法,这些方法比文献中已有的方法更简单且计算成本更低.我们得到了离散偏差的一个下界,如果一个U 型设计的离散偏差值达到这个下界,那么该设计是—个均匀设计.我们建立了均匀设计与组合设计理论中一致可分解设计之间的联系.通过一致可分解设计,我们提出了一些构造均匀设计的新方法,同时也给出了许多均匀设计存在的无穷类.     

可卷型L_2-偏差的水平置换方法构造混合水平设计

提出了一种通过置换因子的水平来构造具有较小可卷型L_2-偏差的混合水平均匀设计的新方法.首先建立了混合水平设计的平均可卷型L_2-偏差与广义字长型的定量关系,并以具有较小广义字长型的混合水平设计为初始设计,对其作水平置换,计算其可卷型L_2-偏差,找到具有最小的可卷型L_2-偏差的设计就是相对较好的设计.为了使算法更加有效,还运用了可卷型L_2-偏差的两个性质.数值结果显示通过这种方法构造的设计在可卷型L_2-偏差下表现良好.     

Uniform exponential convergence of sample average random functions under general sampling with applications in stochastic programming

Sample average approximation (SAA) is one of the most popular methods for solving stochastic optimization and equilibrium problems. Research on SAA has been mostly focused on the case when sampling is independent and identically distributed (iid) with exceptions (Dai et al. (2000) [9], Homem-de-Mello (2008) [16]). In this paper we study SAA with general sampling (including iid sampling and non-iid sampling) for solving nonsmooth stochastic optimization problems, stochastic Nash equilibrium problems and stochastic generalized equations. To this end, we first derive the uniform exponential convergence of the sample average of a class of lower semicontinuous random functions and then apply it to a nonsmooth stochastic minimization problem. Exponential convergence of estimators of both optimal solutions and M-stationary points (characterized by Mordukhovich limiting subgradients (Mordukhovich (2006) [23], Rockafellar and Wets (1998) [32])) are established under mild conditions. We also use the unform convergence result to establish the exponential rate of convergence of statistical estimators of a stochastic Nash equilibrium problem and estimators of the solutions to a stochastic generalized equation problem.     

Additive hazards regression under generalized case-cohort sampling

Case-cohort design usually requires the disease rate to be low in large cohort study,although it has been extensively used in practice.However,the disease with high rate is frequently observed in many clinical studies.Under such circumstances,it is desirable to consider a generalized case-cohort design,where only a fraction of cases are sampled.In this article,we propose the inference procedure for the additive hazards regression under the generalized case-cohort sampling.Asymptotic properties of the proposed estimators for the regression coefcients are established.To demonstrate the efectiveness of the generalized case-cohort sampling,we compare it with simple random sampling in terms of asymptotic relative efciency.Furthermore,we derive the optimal allocation of the subsamples for the proposed design.The fnite sample performance of the proposed method is evaluated through simulation studies.     

A New Family of Estimators for Random Walk Problems

In this paper the weighted uniform sampling technique introducedby Powell & Swann (1966) is extended to random walk problemsand generalized. The family so defined includes, as specialcases, not only weighted uniform estimators, but traditionalunbiased importance sampling estimators and an interesting newbiased importance sampling estimator. Model problem analysisshows that biased importance sampling offers the promise ofuniformly low mean square error over a large spectrum of problemtypes.     

A matrix pencil approach to the existence of compactly supported reconstruction functions in average sampling

The aim of this work is to solve a question raised for average sampling in shift-invariant spaces by using the well-known matrix pencil theory. In many common situations in sampling theory, the available data are samples of some convolution operator acting on the function itself: this leads to the problem of average sampling, also known as generalized sampling. In this paper we deal with the existence of a sampling formula involving these samples and having reconstruction functions with compact support. Thus, low computational complexity is involved and truncation errors are avoided. In practice, it is accomplished by means of a FIR filter bank. An answer is given in the light of the generalized sampling theory by using the oversampling technique: more samples than strictly necessary are used. The original problem reduces to finding a polynomial left inverse of a polynomial matrix intimately related to the sampling problem which, for a suitable choice of the sampling period, becomes a matrix pencil. This matrix pencil approach allows us to obtain a practical method for computing the compactly supported reconstruction functions for the important case where the oversampling rate is minimum. Moreover, the optimality of the obtained solution is established.     

Fixed Design Nonparametric Regression with Truncated and Censored Data

In this paper we consider a fixed design model in which the observations axe subject to left truncation and right censoring. A generalized product-limit estimator for the conditional distribution at a given covaxiate value is proposed, and an almost sure asymptotic representation of this estimator is established. We also obtain the rate of uniform consistency, weak convergence and a modulus of continuity for this estimator.Applications include trimmed mean and quantile function estimators.