基于均匀设计抽样遗传算法求解背包问题

众所周知,遗传算法的运行机理及特点是具有定向制导的随机搜索技术,其定向制导的原则是:导向以高适应度模式为祖先的"家族"方向.以此结论为基础,利用均匀设计抽样的理论和方法,对遗传算法中的交叉操作进行了重新设计,给出了一个新的GA算法,称之为均匀设计抽样遗传算法.最后将均匀设计抽样遗传算法应用于求解背包问题,并与简单遗传算...     

非线性守恒方程的两种稳定化谱元方法

考虑非线性守恒方程的高阶数值解法,介绍了基于谱元法的两种稳定性方法,一种是谱粘性消去法(SVV),另一种是过滤法.在SVV方法中,我们推广并分析了传统的基于单区域的SVV算子的定义.在过滤法中,我们分析了SVV-H elm holtz过滤算子的性质.文中从分析和计算两方面对两种方法进行了比较,建立了两者之间的关系.最后通过一系列数值试验说明方法的有效性.     

一类可积的微分方程

本文系统地研究了一类线性泛函微分方程(组)的基本理论.利用分析原理,借助变量替换,迭代等方法,讨论这类方程一些非线性情形的可解性和解的性质,获得相应的通解公式.推广了有关文献的结果.     

性质(u)在序列空间ces_p(E_n)中的提升

主要研究了性质(u)从Banach空间En到序列空间cesp(En)的提升问题.     

“网民知多少?”——中国互联网络信息中心全国调查抽样方案设计

互联网时代 ,国家和相关企业掌握互联网使用的动态变化情况是非常重要的。本设计采用了多阶段、分层、PPS与等概率相结合的混合抽样方法 ,考虑了时间和经费的限制 ,克服了缺少完整抽样框等困难 ,使抽样的科学性和可操作性得到了较好的结合 ,为中国互联网络信息中心的全国居民上网情况调查提供了切实可行的抽样方案     

基于组合预测模型在高校招生人数预测中的应用研究

引进诱导有序加权平均算子,建立诱导有序加权平均组合预测模型,借助它对四川省普通高等学校招生人数进行预测.通过两个误差指标值的比较,说明这种方法的合理性,从而为教育部门制定相关的就业体制提供依据.     

多目标调查的MPPS平衡抽样方法研究

平衡抽样是广受关注的利用辅助信息改善样本结构的抽样方法.MPPS抽样在多目标调查中采用的与多变量规模成比例的入样概率,可以在平衡抽样中精确满足.基于MPPS抽样和平衡抽样的性质,文章提出多目标调查下的MPPS平衡抽样,该方法主要思想是将多个调查变量的辅助信息在确定入样概率和随机抽样过程中同时使用,提高HT估计量精度.模拟分析得出MPPS平衡抽样优于等概率平衡抽样、MPPS系统抽样和MPPS泊松抽样.同时,讨论了MPPS平衡抽样HT估计量的理论性质,对于随机排列的总体,给出适用的近似方差和方差估计.最后,以我国主要畜禽调查为例,设计一套符合中国现实的多目标调查方案,用MPPS平衡抽样抽取样本,构造主要畜禽存栏量的HT估计及方差估计,为中国政府统计调查提供新选择.     

统计趣题:一次失败的抽样调查

数理统计研究的主要问题之一是:研究如何抽样(抽多少、怎样抽)才能使样本具有很好的代表性,这是抽样方法问题.由于样本来自总体,因此它应当具有总体的性质,并且能正确地反映总体的性质;但样本仅仅是总体的一部分,因而难以全面反映总体的性质.用样     

对性质(ω)等价性的研究

性质(ω)是Weyl定理的一种变形.文章中将算子的一致Fredholm指标性质用于性质(ω)的判定中.根据一致Fredholm指标性质定义出一种新的谱集,通过该谱集和算子的拓扑一致降标之间的关系,给出了有界线性算子与其共轭算子同时满足性质(ω)的充要条件.之后,研究了算子矩阵的(ω)性质.     

群中的一个组合问题

设 G 是一个有限 Abelian 群,c(G)是最小正整数使得 G 的任一合于|S|(?)c(G),0(?)S 的子集 S 均有性质:G 的每一个元均可表为 S 的某些元之和.本文确定了一大类 Abelian 群 G 相应的 c(G).对大部分有限交换 P-群 G,c(G)已由本文确定.     

On the Ranked-Set Sampling M-Estimates for Symmetric Location Families

The ranked-set sampling (RSS) is applicable in practical problems where the variable of interest for an observed item is costly or time-consuming but the ranking of a set of items according to the variable can be easily done without actual measurement. In this article, the M-estimates of location parameters using RSS data are studied. We deal mainly with symmetric location families. The asymptotic properties of M-estimates based on ranked-set samples are established. The properties of unbalanced ranked-set sample M-estimates are employed to develop the methodology for determining optimal ranked-set sampling schemes. The asymptotic relative efficiencies of ranked-set sample M-estimates are investigated. Some simulation studies are reported.     

序集抽样中$M$估计分布的随机加权逼近

序集抽样是一种适用于准确测量花费太高而排序费用可以忽略不记时的一种抽样方法.讨论了序集抽样下的对于一般分布族M估计的相合性和渐近正态性并且通过随机加权的方法来估计M估计的分布.     

Estimation of the Common Mean of a Bivariate Normal Population

In this paper we discuss the problem of estimating the common mean of a bivariate normal population based on paired data as well as data on one of the marginals. Two double sampling schemes with the second stage sampling being either a simple random sampling (SRS) or a ranked set sampling (RSS) are considered. Two common mean estimators are proposed. It is found that under normality, the proposed RSS common mean estimator is always superior to the proposed SRS common mean estimator and other existing estimators such as the RSS regression estimator proposed by Yu and Lam (1997, Biometrics, 53, 1070–1080). The problem of estimating the mean Reid Vapor Pressure (RVP) of regular gasoline based on field and laboratory data is considered.     

优良抽样设计下Logistic分布中参数的极大似然估计

当研究目标的实际测量具有不可修复的破坏性或耗资巨大时,有效的抽样设计将是一项重要的研究课题.在统计推断方面,排序集抽样(RSS)被视为一种比简单随机抽样(SRS)更为有效的收集数据的方式.动态极值RSS (MERSS)是一种修正的RSS.文章在SRS和MERSS下研究了Logistic分布中参数的极大似然估计(MLEs).在这两种抽样下证明了该分布中位置参数和刻度参数的MLEs的存在性和唯一性,并计算了所含参数的Fisher信息量和Fisher信息矩阵.比较了这两种抽样下对应估计的渐近效率.数值结果表明MERSS下的MLEs一致优于SRS下的MLEs.     

Empirical likelihood for balanced ranked-set sampled data

Ranked-set sampling (RSS) often provides more efficient inference than simple random sampling (SRS). In this article, we propose a systematic nonparametric technique, RSS-EL, for hypothesis testing and interval estimation with balanced RSS data using empirical likelihood (EL). We detail the approach for interval estimation and hypothesis testing in one-sample and two-sample problems and general estimating equations. In all three cases, RSS is shown to provide more efficient inference than SRS of the same size. Moreover, the RSS-EL method does not require any easily violated assumptions needed by existing rank-based nonparametric methods for RSS data, such as perfect ranking, identical ranking scheme in two groups, and location shift between two population distributions. The merit of the RSS-EL method is also demonstrated through simulation studies. This work was supported by National Natural Science Foundation of China (Grant No. 10871037)     

Tests of perfect judgment ranking using pseudo-samples

Ranked set sampling (RSS) is a sampling approach that can produce improved statistical inference when the ranking process is perfect. While some inferential RSS methods are robust to imperfect rankings, other methods may fail entirely or provide less efficiency. We develop a nonparametric procedure to assess whether the rankings of a given RSS are perfect. We generate pseudo-samples with a known ranking and use them to compare with the ranking of the given RSS sample. This is a general approach that can accommodate any type of raking, including perfect ranking. To generate pseudo-samples, we consider the given sample as the population and generate a perfect RSS. The test statistics can easily be implemented for balanced and unbalanced RSS. The proposed tests are compared using Monte Carlo simulation under different distributions and applied to a real data set.     

Parametric estimation for the simple linear regression model under moving extremes ranked set sampling design

Cost effective sampling design is a major concern in some experiments especially when the measurement of the characteristic of interest is costly or painful or time consuming.Ranked set sampling(RSS) was first proposed by McIntyre [1952. A method for unbiased selective sampling, using ranked sets. Australian Journal of Agricultural Research 3, 385-390]as an effective way to estimate the pasture mean. In the current paper, a modification of ranked set sampling called moving extremes ranked set sampling(MERSS) is considered for the best linear unbiased estimators(BLUEs) for the simple linear regression model. The BLUEs for this model under MERSS are derived. The BLUEs under MERSS are shown to be markedly more efficient for normal data when compared with the BLUEs under simple random sampling.     

Log-logistic parameters estimation using moving extremes ranked set sampling design

In statistical parameter estimation problems, how well the parameters are estimated largely depends on the sampling design used. In the current paper, a modification of ranked set sampling(RSS) called moving extremes RSS(MERSS) is considered for the estimation of the scale and shape parameters for the log-logistic distribution. Several traditional estimators and ad hoc estimators will be studied under MERSS. The estimators under MERSS are compared to the corresponding ones under SRS. The simulation results show that the estimators under MERSS are significantly more efficient than the ones under SRS.     

Group sequential comparison of two binomial proportions under ranked set sampling design

Ranked set sampling (RSS) is a technique for incorporating auxiliary (concomitant) information into estimation and testing procedures right at the design stage. In this paper, we propose group sequential testing procedures for comparing two treatments with binary outcomes under an RSS scheme with perfect ranking. We compare the power, the average sample sizes and type I errors of the proposed tests to those of the group sequential tests based on simple random sampling schemes. We illustrate the usefulness of the methodology by using data from a clinical trial on leukemia.     

Local sampling set conditions in weighted shift-invariant signal spaces

How to represent a continuous signal in terms of a discrete sequence is a fundamental problem in sampling theory. Most of the known results concern global sampling in shift-invariant signal spaces. But in fact, the local reconstruction from local samples is one of the most desirable properties for many applications in signal processing, e.g. for implementing real-time reconstruction numerically. However, the local reconstruction problem has not been given much attention. In this article, we find conditions on a finite sampling set X such that at least in principle a continuous signal on a finite interval is uniquely and stably determined by their sampling value on the finite sampling set X in shift-invariant signal spaces.