异方差性的方差分析及其应用

张荣基．异方差性的方差分析及其应用本文就家猪育种试验比较中提出的问题,给出了单因素的异方差性的方差分析方法及应用实例     

方差分析与参数估计

方差分析用Ｆ统计量进行检验，Ｆ＝Ｓ２ＡＳ２ｅ，Ｓ２Ａ为因素Ａ的组间方差，Ｓ２ｅ为剩余方差．实际上，Ｓ２Ａ、Ｓ２ｅ都是方差σ２的估计量．本文简介组内观测值个数相等情形单因素方差分析的参数估计方法．（双因素方差分析的估计方法类似）１．组间方差等于样本平均...     

调查表可重复性信度研究方法

本文结合安庆市１９９２年部分乡镇卫技人员二次心态调查资料，介绍了一种调查表内计量测量的条目的可重复性信度分析方法──方差分析法，以及定性测量的条目两次调查结果一致度分析方法，对影响调查表可重复性的主要因素进行了讨论，强调了调查表信度分析的意义这对于我们在大型现场研究中提高调查表设计能力，提高测量的效度与精度有重要理论指导意义．     

破坏性测量系统波动源的分析

测量系统分析是质量改进活动中的一个重要问题,评价测量系统精确度的标准方法是量具的重复性和再现性(R&R)研究,这种研究通常采用试验设计方法,而且对测量对象可以重复测量。然而在工业实践中,当遇到破坏性测量时,这种标准的R&R研究方法是无法实施的。本文给出了破坏性测量中测量系统波动源分析的一种方法,首先分析了破坏性测量中进行量具R&R研究的前提条件,进而给出了进行量具R&R研究的随机效应模型和方差分析方法;最后通过应用Minit-ab.14软件,对工业实践中的一个应用实例进行了分析。     

中国股票市场“周内效应”的再研究

本文避免了通常利用方差分析方法检验周内效应时的不足之处,利用考虑到异方差情况ARCH模型,通过采用虚拟变量,在研究沪深两市日收益率的周内效应的基础上,进一步考察其方差变动的周内效应。通过实证分析发现沪市存在显著为正的星期五效应,深市存在显著为负的星期一效应与显著为正的星期五效应,并且深市日收益的方差变化也存在一定的周内效应。     

基于最大熵原理的测量不确定度商概率建模及计算

测量数学模型建立后,测量不确定度评定需考虑输入量的不确定度及分布的传递性.若测量模型为幂指数积模型,通常先将输入量线性化,再采用合成方差的方法计算被测量的不确定度,方法计算简便,但未揭示不确定度随分布传递这一现象.针对问题,首先运用最大熵原理,计算测量仪器分辨力已知时,输入量的概率密度函数,其结果显示输入量服从矩形分布;其次重点推导了输入量与被测量之间为商概率模型时,被测量的概率密度函数、估计值和测量不确定度,并给出不确定度评定的具体步骤;最后利用恒流源测量高精密小电阻实例的对比分析,说明了该不确定度评定模型的可行性和有效性.     

一种单因子异方差模型的分析方法

本文提出了一种单因子异方差模型,导出这种异方差分析方法,并给出了模型中均值与方差的估计.通过对仿真和数字化设计模型数据进行检验,结果表明,采用此方法比传统方法更有效.     

多元HotellingT~2检验在高校人才流动归因研究中的应用

田晓明．多元Ｈｏｔｅｌｌｉｎｇ Ｔ２ 检验在高校人才流动归因研究中的应用．对均值差异的显著性检验是心理学研究经常涉及到的问题。当指标之间相互独立且等方差时,我们可以采用方差分析方法进行检验。若指标之间不独立或方差不等时,就应当采用多元Ｈｏｔｅｌｌｉｎｇ Ｔ２ 统计量来进行检验。本文通过理论推导及例证（高校人才流动归因研究）形式对多元Ｈｏｔｅｌｌｉｎｇ Ｔ２ 统计量的应用价值进行了一些探索和研究     

平衡随机模型中方差分量的非负估计

众所周知, 对于平衡随机模型, 方差分量的方差分析估计为一致最小方差无偏估计. 本文基于方差分量的方差分析估计, 构造了一个二次不变估计类, 它包含了一些常用重要估计. 证明了该估计类在一定条件下在均方误差意义下一致优于方差分析估计, 并在此估计类基础上, 给出了方差分量的两种非负估计, 它们在均方误差意义下分别一致优于方差分析估计和限制极大似然估计, 且有显式解、容易计算.     

单指标模型异方差检验（英文）

在可加回归模型中,高维回归分析一般采用单指标模型.该模型与参数模型相比更加灵活,同时避免了维数灾难,因为单指标将标准变量向量的维数降低为单变量指标.本文构建了一个带有函数型误差项的单指数回归模型用于检验单指标模型的异方差性.由于回归模型的有效推断要求在存在异方差的情况下考虑异方差,本文提出了检验单指标模型方差不变性的假设.将Levene检验和无限因子水平的方差分析理论结合得到检验统计量用来评估方差同质性.模拟研究显示与已有方法相比,所提检验统计量适用于多种情形.最后将本文的方法应用于分析一组实际数据.     

多元系统马氏田口方法的诊断与分析研究

马氏田口方法是研究多元系统的一种新方法,主要用于建立和优化多元系统的测量表,以便采用优化后的测量表进行诊断/预测。本文基于马氏田口方法四大基本步骤和MYT正交分解对马氏田口方法进行了拓展研究。首先,介绍了马氏田口方法的四大基本步骤;接着介绍了MYT正交分解法,并将MYT正交分解法应用于多元系统的马氏田口方法异常值潜在原因分析;最后,利用拓展后的马氏田口方法对某医院血粘度诊断系统进行了优化,同时对利用优化后的测量表进行诊断时发现的异常值进行了潜在原因分析,取得了良好的效果。     

The impact of mismeasurement in performance benchmarking: A Monte Carlo comparison of SFA and DEA with different multi-period budgeting strategies

Performance-based budgeting has received increasing attention from public and for-profit organizations in an effort to achieve a fair and balanced allocation of funds among their individual producers or operating units for overall system optimization. Although existing frontier estimation models can be used to measure and rank the performance of each producer, few studies have addressed how the mismeasurement by frontier estimation models affects the budget allocation and system performance. There is therefore a need for analysis of the accuracy of performance assessments in performance-based budgeting. This paper reports the results of a Monte Carlo analysis in which measurement errors are introduced and the system throughput in various experimental scenarios is compared. Each scenario assumes a different multi-period budgeting strategy and production frontier estimation model; the frontier estimation models considered are stochastic frontier analysis (SFA) and data envelopment analysis (DEA). The main results are as follows: (1) the selection of a proper budgeting strategy and benchmark model can lead to substantial improvement in the system throughput; (2) a “peanut butter” strategy outperforms a discriminative strategy in the presence of relatively high measurement errors, but a discriminative strategy is preferred for small measurement errors; (3) frontier estimation models outperform models with randomly-generated ranks even in cases with relatively high measurement errors; (4) SFA outperforms DEA for small measurement errors, but DEA becomes increasingly favorable relative to SFA as the measurement errors increase.     

Efficiency measurement using independent component analysis and data envelopment analysis

Efficiency measurement is an important issue for any firm or organization. Efficiency measurement allows organizations to compare their performance with their competitors’ and then develop corresponding plans to improve performance. Various efficiency measurement tools, such as conventional statistical methods and non-parametric methods, have been successfully developed in the literature. Among these tools, the data envelopment analysis (DEA) approach is one of the most widely discussed. However, problems of discrimination between efficient and inefficient decision-making units also exist in the DEA context (Adler and Yazhemsky, 2010). In this paper, a two-stage approach of integrating independent component analysis (ICA) and data envelopment analysis (DEA) is proposed to overcome this issue. We suggest using ICA first to extract the input variables for generating independent components, then selecting the ICs representing the independent sources of input variables, and finally, inputting the selected ICs as new variables in the DEA model. A simulated dataset and a hospital dataset provided by the Office of Statistics in Taiwan’s Department of Health are used to demonstrate the validity of the proposed two-stage approach. The results show that the proposed method can not only separate performance differences between the DMUs but also improve the discriminatory capability of the DEA’s efficiency measurement.     

The Use of Dimensional and Similarity Analyses in the Propagation of Uncertainties: A Physical Example

Dimensional and similarity analyses are used in physics and engineering, specially in fluid mechanics, to reduce the dimension of the input variable space with no loss of information. Here, we apply these techniques to the propagation of uncertainties for computer codes by the Monte Carlo method, in order to reduce the variance of the estimators of the parameters of the output variable distribution. In the physics and engineering literature, dimensional analysis is often formulated intuitively in terms of physical quantities or dimensions such as time, longitude, or mass; here we use the more rigorous and more abstract generalized dimensional analysis of Moran and Marshek. The reduction of dimensionality is only successful in reducing estimator variance when applying variance-reduction techniques and not when using ordinary random sampling. In this article we use stratified sampling, and the key point of the success of the reduction in dimensionality in improving the precision of the estimates is a better measurement of the distances betwen the outputs, for given inputs. We illustrate the methodology with an application to a physical problem, a radioactive contaminant transport code. A substantial variance reduction is achieved for the estimators of the mean, variance, and distribution function of the output. Last, we present a discussion on which conditions are necessary for the method to be successful.     

Mean–variance approximations to expected utility

It is often asserted that the application of mean–variance analysis assumes normal (Gaussian) return distributions or quadratic utility functions. This common mistake confuses sufficient versus necessary conditions for the applicability of modern portfolio theory. If one believes (as does the author) that choice should be guided by the expected utility maxim, then the necessary and sufficient condition for the practical use of mean–variance analysis is that a careful choice from a mean–variance efficient frontier will approximately maximize expected utility for a wide variety of concave (risk-averse) utility functions. This paper reviews a half-century of research on mean–variance approximations to expected utility. The many studies in this field have been generally supportive of mean–variance analysis, subject to certain (initially unanticipated) caveats.     

A hybrid analysis approach for finite-capacity queues with general inputs and phase type service

A new analysis method for queueing systems with general input stream and phase type service time distributions is introduced. The approach combines discrete event simulation and numerical analysis of continuous time Markov chains. Simulation is used to represent the arrival process, whereas the service process is analyzed with numerical techniques. In this way the state of the system is characterized by a probability vector rather than by a single state. The use of a distribution vector reduces the variance of result estimators such that the width of confidence intervals is often reduced compared to discrete event simulation. This, in particular, holds for measures based on rare events or states with a small probability. The analysis approach can be applied for a wide variety of result measures including stationary, transient and accumulated measures. This revised version was published online in June 2006 with corrections to the Cover Date.     

复杂可修复系统故障数据分析处理中的两个问题

马良河等．复杂可修复系统故障数据分析处理中的两个问题．数理统计与管理，１９９８，１７（３），３７～４０．本文给出了利用部件的故障数据对复杂可修复系统的可靠性状态进行分析和估计时应首先解决的两个问题：应选取合适的故障数据及应分析系统部件的固有属性对故障数据的影响。进而给出了这两个问题的解决方法及对系统性能的分析方法。最后针对飞机发动机的一组故障数据给出了计算实例。所用工具主要是假设检验和方差分析的理论     

An Algorithm for a Letter-Based Representation of All-Pairwise Comparisons

All-pairwise comparisons among a set of t treatments or groups are one of the most frequent tasks in applied statistics. Users of statistical software are accustomed to the familiar lines display, in which treatments that do not differ significantly, are connected by a common line or letter. Availability of the lines display is restricted mainly to the balanced analysis of variance setup. This limited availability is at stark variance with the diversity of statistical methods and models, which call for multiple comparisons. This article describes a general method for graphically representing any set of t(t?1)/2 all-pairwise significance statements (p values) for t treatments by a familiar letter display, which is applicable regardless of the underlying data structure or the statistical method used for comparisons. The method reproduces the familiar lines display in case of the balanced analysis of variance. Its broad applicability is demonstrated using data from an international multienvironment wheat yield trial and from a fish catching survey.     

环保意识调查问卷的Logistic模型

本文应用多元统计分析中的方差分析 ,logistic模型等方法对环保意识调查问卷反馈的信息进行统计分析 ,建立被调查企业对环境保护意识的logistic模型 ,进而可对企业家的环境保护意识的好坏进行判断。