中位数排序集抽样下总体均值的比率估计方法

文章探讨了中位数排序集抽样下总体均值比率估计的方法。通过对不同抽样方法的估计量进行效率对比,证明在总体分布为正态分布的情况下,基于中位数排序集样本的比率估计比随机抽样的样本比率估计效率高。     

基于排序集样本的Bayes统计推断

从参数估计的角度,讨论了基于排序集抽样的Bayes估计,并进一步与传统的Bayes估计相比较,从估计的相对效率角度说明了基于排序集抽样的Bayes估计的优良性。     

基于不均匀数据的密度偏差抽样改进算法

针对不均匀数据集的抽样问题,已有随机抽样算法、基于固定网格划分的单维度算法、基于可变网格划分的单维度算法,但仍无法更好地反映数据分布特征问题。在数据挖掘的实际应用中,数据规模越来越大,数据类型也越来越复杂,存在系统整体开销大、时间运行成本高等问题。提出并实现了基于不均匀数据的密度偏差抽样改进算法（IDDS）,通过引入网格单元密度和三角函数,从而达到较好的密度偏差抽样效果。实验结果发现,IDDS算法抽样效果更好,提取的样本质量更高,有效保证了不均匀数据的分布特征。与原始的密度偏差抽样算法(DDS)相比,应用IDDS算法的效率更高。     

大数据下Logistic模型的最优子抽样算法研究

抽样方法在大数据研究中发挥着重要作用,子抽样作为其中之一可以非常高效地解决数据量大的问题,无论是线性回归模型还是Logistic回归模型都有相应的子抽样方法。本文使用大数据下基于二元Logistic模型的两种子抽样方法,分别是普通子抽样方法和两阶段最优子抽样方法,并利用实际数据评估了算法的优良性,得出以下结论：基于两阶段子抽样算法建立的Logistic回归模型在估计精度上优于基于普通子抽样建立的模型;基于L最优准则下的子抽样虽然比基于A最优准则下的子抽样估计精度略低,但耗费的运算时间更短。     

抽样平均误差计算公式的证明

目前,大多数统计学教材中都没有给出抽样平均误差的计算公式的理论证明.本文针对重复抽样,不重复抽样考虑抽样顺序,不重复抽样不考虑抽样顺序这三种情况下的抽样平均误差的计算公式给予证明.     

PISA抽样方法简介与PISA2015中国抽样实践

学生能力国际评价(PISA)是由经济合作与发展组织发起的大规模国际教育评价项目,抽样对评价结果的国际可比性至为关键。本文对PISA的抽样方法与理念、抽样的各个环节与技术标准进行介绍,并简要介绍PISA2015中国抽样的实践情况,基于PISA的抽样经验,对我国国内大型教育测评项目未来抽样的数据收集等环节提出一些设想与展望。     

均匀设计抽样在遗传算法中的应用

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。而遗传算法中的交叉操作就是在高适应度模式中寻找最优解的操作。文章以此结论为基础,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。使其能随机、均匀地搜索高适应度模式空间,能有效提高搜索的效率。称这样得到的新的遗传算法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函数优化问题,并与简单遗传算法和佳点集遗传算法进行比较。通过模拟比较。可以看出新的算法不但提高了算法的速度和精度．而且避免了早熟现象。     

基于灰色-Vague集的软件质量综合评价

结合ISO 9216软件度量模型,在模糊综合评价方法的基础上,提出基于灰色-Vague集的软件质量综合评价方法,即用Vague集对模糊集进行改进,并利用Vague集理论和灰色理论建立软件质量模型。该模型能有效处理软件质量评价过程中的模糊性和人脑综合判断的灰色综合分析性质,为待评方案排序的解析化、定量化提供有效的手段。     

信号与系统中基于抽样定理的教学内容总结与分析

文章针对信号与系统中抽样定理的教学内容进行了较详细的总结和分析,补充和扩展了抽样定理的相关知识内容,增加了连续周期信号的抽样分析、窄带信号的抽样特性以及基于压缩感知的抽样等知识的讨论。     

基于Vague集的模糊多属性决策方法

提出基于Vague集理论来处理模糊多属性决策问题的一些方法.对Chen和Tan（Fuzzy setsand Systems 67（1994））163-172）、Hong和Choi（Fuzzy sets and Systems 114（2000）103-113）等两篇论文中所提出的记分函数和精确函数进行分析验证和改进,并给出了处理模糊多属性决策问题的新方法.结果表明：新方法能够使得决策排序是严格有序的,避免了排序中不易决策的问题.     

具有部分缺失数据时两个威布尔分布的估计和检验

文中讨论了部分缺失数据两威布尔总体的参数估计和关于总体相同的似然比检验．证明估计的强相合性和渐近正态性,给出似然比检验统计量的极限分布,并探讨了基于精确分布的检验问题．     

随机微分方程改进半隐Milstein方法的稳定性

研究了随机微分方程改进的半隐Milstein方法的均方稳定和渐近稳定性．对线性检验方程,得到了改进的半隐Milstein方法对任意步长△≠〉0均方稳定的充要条件是3/4 ≤ θ ≤ 1．证明了当方法的步长充分小时,方法能保持原系统的渐近稳定性．     

The Asymptotic Covariance Matrix and its Use in Simulation Studies

The asymptotic performance of structural equation modeling tests and standard errors are influenced by two factors: the model and the asymptotic covariance matrix Γ of the sample covariances. Although most simulation studies clearly specify model conditions, specification of Γ is usually limited to values of univariate skewness and kurtosis. We illustrate that marginal skewness and kurtosis are not sufficient to adequately specify a nonnormal simulation condition by showing that asymptotic standard errors and test statistics vary substantially among distributions with skewness and kurtosis that are identical. We argue therefore that Γ should be reported when presenting the design of simulation studies. We show how Γ can be exactly calculated under the widely used Vale–Maurelli transform. We suggest plotting the elements of Γ and reporting the eigenvalues associated with the test statistic. R code is provided.     

Using Monte Carlo Normal Distributions to Evaluate Structural Models With Nonnormal Data

Statistical theories of goodness-of-fit tests in structural equation modeling are based on asymptotic distributions of test statistics. When the model includes a large number of variables or the population is not from a multivariate normal distribution, the asymptotic distributions do not approximate the distribution of the test statistics very well at small sample sizes. A variety of methods have been developed to improve the accuracy of hypothesis testing at small sample sizes. However, all these methods have their limitations, specially for nonnormal distributed data. We propose a Monte Carlo test that is able to control Type I error with more accuracy compared to existing approaches in both normal and nonnormally distributed data at small sample sizes. Extensive simulation studies show that the suggested Monte Carlo test has a more accurate observed significance level as compared to other tests with a reasonable power to reject misspecified models.     

如何讲好时域取样定理

时域取样定理是"信号与系统"和"数字信号处理"等课程的重要内容,但是,目前高等院校普遍使用的国内外经典教材对这部分内容论述不够清晰,没有证明连续时间信号与离散时间信号频谱的关系,尤其是引入了理想冲激取样过程,物理意义不明确,影响了学生理解。本文对时域取样定理进行了深入的思考,提出了两种讲解思路,教学实践表明,对增强学生对该内容的理解,提高授课质量,有较强的针对性。     

Applications of the Analytically Derived Asymptotic Standard Errors of Item Response Theory Item Parameter Estimates

The analytically derived asymptotic standard errors (SEs) of maximum likelihood (ML) item estimates can be approximated by a mathematical function without examinees' responses to test items, and the empirically determined SEs of marginal maximum likelihood estimation (MMLE)/Bayesian item estimates can be obtained when the same set of items is repeatedly estimated from the simulation (or resampling) test data. The latter method will result in rather stable and accurate SE estimates as the number of replications increases, but requires cumbersome and time-consuming calculations. Instead of using the empirically determined method, the adequacy of using the analytical-based method in predicting the SEs for item parameter estimates was examined by comparing results produced from both approaches. The results indicated that the SEs yielded from both approaches were, in most cases, very similar, especially when they were applied to a generalized partial credit model. This finding encourages test practitioners and researchers to apply the analytically asymptotic SEs of item estimates to the context of item-linking studies, as well as to the method of quantifying the SEs of equating scores for the item response theory (IRT) true-score method. Three-dimensional graphical presentation for the analytical SEs of item estimates as the bivariate function of item difficulty together with item discrimination was also provided for a better understanding of several frequently used IRT models.     

Using the Standardized Root Mean Squared Residual (SRMR) to Assess Exact Fit in Structural Equation Models

We examine the accuracy of p values obtained using the asymptotic mean and variance (MV) correction to the distribution of the sample standardized root mean squared residual (SRMR) proposed by Maydeu-Olivares to assess the exact fit of SEM models. In a simulation study, we found that under normality, the MV-corrected SRMR statistic provides reasonably accurate Type I errors even in small samples and for large models, clearly outperforming the current standard, that is, the likelihood ratio (LR) test. When data shows excess kurtosis, MV-corrected SRMR p values are only accurate in small models (p = 10), or in medium-sized models (p = 30) if no skewness is present and sample sizes are at least 500. Overall, when data are not normal, the MV-corrected LR test seems to outperform the MV-corrected SRMR. We elaborate on these findings by showing that the asymptotic approximation to the mean of the SRMR sampling distribution is quite accurate, while the asymptotic approximation to the standard deviation is not.     

RTK-GPS在工程测量中的应用

通过工程实例分析了RTK-GPS在工程测量中应用时的作业流程、性能、效率和精度。     

Alternative Monte Carlo Approach for General Global Illumination

Ithasbeenwellknownthatglobalilluminationsimu latesthelighttransportamongallthesurfacesandthelightsourcesinavirtualsceneconsideringdiffusereflection/refrac tionandspecularreflection/refraction .Duetosimplicity,generalityanddimensionalindependence,MonteCarlomethodorrandomwalkmethodisoneofthemostimportantmethodsforsolvingthedifficultglobalillumination .Inpartic ular,MonteCarloisthelastresortwhenallotheranalyticalornumericalmethodsfail[1 ] .　　Lighttransportormultiplelightinteractionsamongthesu…