Examining Intermediate Omitted Levels in Hierarchical Designs via Latent Variable Modeling

This article outlines an interval estimation procedure that can be used in a 3-level setting to evaluate the proportion of outcome variance attributable to the second level of clustering. The method is useful for examining the necessity of including a possibly omitted intermediate level of nesting in analyses of data from a multilevel study, and represents an informative addendum to current statistical tests of second-level variance. The approach is developed within the framework of latent variable modeling and can be used as an aid in the process of choosing between 2-level and 3-level models in a hierarchical design. The discussed procedure is illustrated with an empirical example.     

失效概率的E-Bayes估计与多层Bayes估计

基于失效概率的E-Bayes估计,在无失效数据情形下给出了失效概率的多层Bayes估计,且讨论了失效概率的多层Bayes估计的性质——E-Bayes估计与多层Bayes估计的关系。数值算例和应用实例进一步表明,E-Bayes估计和多层Bayes估计比较接近。     

Evaluation of Validity and Reliability for Hierarchical Scales Using Latent Variable Modeling

A latent variable modeling method is outlined, which accomplishes estimation of criterion validity and reliability for a multicomponent measuring instrument with hierarchical structure. The approach provides point and interval estimates for the scale criterion validity and reliability coefficients, and can also be used for testing composite or simple hypotheses about these coefficients. The proposed method is illustrated with a numerical example.     

Lower Level Mediation Effect Analysis in Two-Level Studies: A Note on a Multilevel Structural Equation Modeling Approach

This article presents a didactic discussion of a multilevel covariance structure modeling approach to estimation of lowest level mediation effect indexes in two-level studies. The procedure is useful when addressing questions about relations among total and indirect effects between variables of interest while accounting for the hierarchical structure of analyzed data. The discussed method also permits interval estimation and hypothesis tests with respect to related quantities of relevance when evaluating mediated effects with clustered data, and is illustrated on a two-level data set.     

熵损失函数下Lomax分布形状参数的Bayes估计

研究了在熵损失函数下,两参数Lomax分布中形状参数的Bayes估计、多层Bayes估计及E-Bayes估计。     

截尾Gamma分布无失效数据的贝叶斯可靠性分析

对无失效数据（ti,ni）,在时刻ti的失效概率pi=P{T     

平方损失下Lomax分布形状参数的Bayes估计

研究了在平方损失下,两参数Lom ax分布中形状参数的Bayes估计、多层Bayes估计及E-Bayes估计.     

截尾Gamma分布无失效数据的贝叶斯可靠性分析

对无失效数据(ti,ni),在时刻ti的失效概率pi=P{T刍ti}的先验分布为截尾Gamma分布时,给出了pi的Bayes估计和多层Bayes估计,从而得到无失效数据情形可靠度的估计.     

无失效数据下的参数及可靠度估计

对指数分布无失效数据的失效率,在先验分布为Gamma分布时,在引进失效信息后,给出了多层Bayes估计,并给出了可靠度的估计.     

施工项目成本控制对策的层次分析

采用层次分析法(AHP)对成本控制对策进行全面分析，并通过建立施工项目成本控制体系模型和对其进行一致性检验，总结出各种成本控制措施的重要权值，得出降低成本和科学排序的措施参数。     

无失效数据下的参数及可靠度估计

对指数分布无失效数据的失效率,在先验分布为Gamma分布时,在引进失效信息后,给出了多层Bayes估计,并给出了可靠度的估计.     

刻度平方误差损失下Pareto分布参数的E Bayes和多层Bayes估计

在对给定容量为n的一个Pareto分布样本X 1,X 2,…,Xn,在刻度平方误差损失函数下,利用先验分布讨论Pareto分布参数λ的E Bayes估计和多层Bayes估计.     

Improved Estimation of Maximal Reliability for Unidimensional Multicomponent Measuring Instruments in Repeated Measure Studies

In repeated measure studies with unidimensional scales, measurement invariance, and specificity stability over time, the specificity variance in each instrument component can be identified. This article describes for that setting an improved point and interval estimation procedure for the maximal reliability coefficient associated with a given set of homogeneous measures. The method is developed within the framework of latent variable modeling and can also be readily used in longitudinal studies for improved point and interval estimation of individual measure reliability and scale reliability at each assessment occasion. The procedure is based on empirically testable conditions and is illustrated with an example.     

Bi-dimension decomposed hidden Markov models for multi-person activity recognition

We present a novel model for recognizing long-term complex activities involving multiple persons. The proposed model, named ＇decomposed hidden Markov model＇ （DHMM）, combines spatial decomposition and hierarchical abstraction to capture multi-modal, long-term dependent and multi-scale characteristics of activities. Decomposition in space and time offers conceptual advantages of compaction and clarity, and greatly reduces the size of state space as well as the number of parameters. DHMMs are efficient even when the number of persons is variable. We also introduce an efficient approximation algorithm for inference and parameter estimation. Experiments on multi-person activities and multi-modal individual activities demonstrate that DHMMs are more efficient and reliable than familiar models, such as coupled HMMs, hierarchical HMMs, and multi-observation HMMs.     

Intrusion detection method based on improved growing hierarchical self-organizing map

Considering that growing hierarchical self-organizing map (GHSOM) ignores the influence of individual component in sample vector analysis, and its accurate rate in detecting unknown network attacks is relatively lower, an improved GHSOM method combined with mutual information is proposed. After theoretical analysis, experiments are conducted to illustrate the effectiveness of the proposed method by accurately clustering the input data. Based on different clusters, the complex relationship within the data can be revealed effectively.     

A Bayesian Approach for Analyzing Hierarchical Data With Missing Outcomes Through Structural Equation Models

Structural equation models are widely appreciated in behavioral, social, and psychological research to model relations between latent constructs and manifest variables, and to control for measurement errors. Most applications of structural equation models are based on fully observed data that are independently distributed. However, hierarchical data with a correlated structure are common in behavioral research, and very often, missing data are encountered. In this article, we propose a 2-level structural equation model for analyzing hierarchical data with missing entries, and describe a Bayesian approach for estimation and model comparison. We show how to use WinBUGS software to get the solution conveniently. The proposed methodologies are illustrated through a simulation study, and a real application in relation to organizational and management research concerning the study of the interrelationships of the latent constructs about job satisfaction, job responsibility, and life satisfaction for citizens in 43 countries.     

论高校武术的层次化教学

层次化教学为更好的开展武术教学带来了新的教学理论与教学手段。层次化教学符合"建构主义"与"有效教学"的教学理念,在高校武术教学中有利于突出学生间的个体差异,有利于学生的自主探究。论文阐述了层次化教学在高校武术教学中应用的意义,从教学目标、教学内容、教学方法、教学评价等四个方面论述了高校武术层次化教学的对策。     

Testing Time-Invariance of Variable Specificity in Repeated Measure Designs Using Structural Equation Modeling

A structural equation modeling method for examining time-invariance of variable specificity in longitudinal studies with multiple measures is outlined, which is developed within a confirmatory factor-analytic framework. The approach represents a likelihood ratio test for the hypothesis of stability in the specificity part of the residual term associated with repeated administration of each measure. The procedure can be used in the search for parsimonious versions of multiwave multiple-indicator models, to test for variable specificity in them, and to examine assumptions underlying particular parameter estimation procedures in repeated measure designs. The outlined method is illustrated with empirical data.     

MIMO-OFDM系统的信道估计方法与发展趋势

本文概述了MIMO-OFDM系统的信道估计方法,即非盲信道估计、盲信道估计和半盲信道估计.非盲信道估计方法收敛速度快,简单且易实现,但信道利用率和系统的传输效率低;盲信道估计方法可有效节省系统带宽,提高频谱利用率,但此方法复杂度较大.而半盲信道估计方法是非盲信道估计和盲信道估计的一个折衷方法,能够很好的兼顾实现复杂度与估计性能.因此,半盲信道估计方法对未来MIMO-OFDM系统的信道估计发展趋势明显,将会日益受到人们的重视.     

Data Mining方法在提高薄板冲压成型回弹预测准确性中的应用

A new method was worked out to improve the precision of springback prediction in sheet metal forming by combining the finite element method (FEM) with the data mining (DM) technique. First the genetic algorithm (GA) was adopted for recognizing the material parameters. Then according to the even design idea, the suitable calculation scheme was confirmed, and FEM was used for calculating the springback. The computation results were compared with experiment data, the difference between them was taken as source data, and a new pattern recognition method of DM called hierarchical optimal map recognition method (HOMR) is applied for summarizing the calculation regulation in FEM. At the end, the mathematics model of the springback simulation was established.Based on the model, the calculation errors of springback can be controlled within 10 % compared with the experimental results.