A Scaled F Distribution as an Approximation to the Distribution of Test Statistics in Covariance Structure Analysis

When the assumption of multivariate normality is violated or when a discrepancy function other than (normal theory) maximum likelihood is used in structural equation models, the null distribution of the test statistic may not be χ² distributed. Most existing methods to approximate this distribution only match up to 2 moments. In this article, we propose 2 additional approximation methods: a scaled F distribution that matches 3 moments simultaneously and a direct Monte Carlo–based weighted sum of i.i.d. χ² variates. We also conduct comprehensive simulation studies to compare the new and existing methods for both maximum likelihood and nonmaximum likelihood discrepancy functions and to separately evaluate the effect of sampling uncertainty in the estimated weights of the weighted sum on the performance of the approximation methods.     

稳健黑箱模型预测反相液相色谱保留方程系数

提出稳健黑箱模型预测反相液相色谱保留方程系数,结果明显优于多元线性回归方法,并与反向传播算法网络方法进行比较.     

一类非线性扰动中立型系统的鲁棒镇定

讨论了一类非线性扰动中立型系统的鲁棒镇定问题．在非线性不确定性满足增益有界条件下，首先给出了自由系统鲁棒稳定的一个充分条件，接着通过构造无记忆状态反馈控制器，给出了闭环系统渐近稳定的充分条件．     

Software review

Amos 3.51. Written by James L. Arbuckle. Distributed by SmallWaters Corporation, 1507 East 53rd Street, Suite 452, Chicago, IL 60615. Regular price, $465, educational price, $325, possible upgrade discounts.     

Evaluation of Test Statistics for Robust Structural Equation Modeling With Nonnormal Missing Data

A 2-stage robust procedure as well as an R package, rsem, were recently developed for structural equation modeling with nonnormal missing data by Yuan and Zhang (2012). Several test statistics that have been used for complete data analysis are employed to evaluate model fit in the 2-stage robust method. However, properties of these statistics under robust procedures for incomplete nonnormal data analysis have never been studied. This study aims to systematically evaluate and compare 5 test statistics, including a test statistic derived from normal-distribution-based maximum likelihood, a rescaled chi-square statistic, an adjusted chi-square statistic, a corrected residual-based asymptotical distribution-free chi-square statistic, and a residual-based F statistic. These statistics are evaluated under a linear growth curve model by varying 8 factors: population distribution, missing data mechanism, missing data rate, sample size, number of measurement occasions, covariance between the latent intercept and slope, variance of measurement errors, and downweighting rate of the 2-stage robust method. The performance of the test statistics varies and the one derived from the 2-stage normal-distribution-based maximum likelihood performs much worse than the other four. Application of the 2-stage robust method and of the test statistics is illustrated through growth curve analysis of mathematical ability development, using data on the Peabody Individual Achievement Test mathematics assessment from the National Longitudinal Survey of Youth 1997 Cohort.     

不确定离散时滞系统的鲁棒H_∞控制

本文主要研究了同时具有状态时滞和输入时滞的不确定离散时滞系统的鲁棒H_∞控制问题。基于二次稳定性理论,采用线性矩阵不等式方法,给出了该系统鲁棒H_∞控制器存在的充分条件和有记忆状态反馈控制器的设计方法。最后利用数值例子说明了该方法的有效性。     

一类不确定非线性广义时滞系统的鲁棒控制与保性能控制

研究了不确定非线性结构扰动的广义系统的鲁棒控制和保性能控制问题．目的是分别设计系统的鲁棒控制器和保性能控制器。应用线性矩阵不等式方法,分别给出了系统的鲁棒控制器和保性能控制器存在的充分条件;并在这些条件可解时,分别给出了鲁棒控制器和保性能控制器的表达式。最后用例子说明了所给方法的应用．     

考虑排污权交易的企业生产决策鲁棒优化

研究面向产品需求不确定的排污权交易企业生产决策。首先,在确定生产技术的条件下,针对产品需求不确定性,建立企业收益函数,然后,基于最小、最大后悔值准则,构建企业生产决策鲁棒模型,求解相对最优鲁棒生产决策,最后,通过数值分析说明本章模型的应用,为企业生产过程中决策者应对不确定决策提供建议,提高企业对排污权交易机制的适应性。     

遥测降雨异常值的三步抗差统计探测

由于遥测降雨系统自身的原因,遥测降雨资料中常有异常值的出现.充分利用降雨分布特征以及抗差统计理论,提出一种三步抗差统计方法探测遥测降雨资料中的异常值.本方法采用Tukey fence统计方法抵御异常值的干扰,用三步的形式以适应降雨资料的分布特征.对面平均降雨量进行的分组,进一步提高方法的探测效率.数据证明,新方法的探测效果较好,且符合水文预报要求.     

Daily Influent Quantity Forecasting Method for Sewage Treatment Plant Considering Uncertain Factors

Daily influent quantity forecasting plays an important role in sewage treatment plant design and operation.Its uncertain factors are classified into three categories including day types,weather conditions and special events,of which the latter two are consicered with a BP(Back Propagation)model.On this basis,the daily period feature is taken into account in the presented model.The data from a practical sewage treatment plant utility is employed to show the effectiveness of the method.