Variance Estimation Using Replication Methods in Structural Equation Modeling With Complex Sample Data

This article discusses replication sampling variance estimation techniques that are often applied in analyses using data from complex sampling designs: jackknife repeated replication, balanced repeated replication, and bootstrapping. These techniques are used with traditional analyses such as regression, but are currently not used with structural equation modeling (SEM) analyses. This article provides an extension of these methods to SEM analyses, including a proposed adjustment to the likelihood ratio test, and presents the results from a simulation study suggesting replication estimates are robust. Finally, a demonstration of the application of these methods using data from the Early Childhood Longitudinal Study is included. Secondary analysts can undertake these more robust methods of sampling variance estimation if they have access to certain SEM software packages and data management packages such as SAS, as shown in the article.     

Instrumental Variables Two-Stage Least Squares (2SLS) vs. Maximum Likelihood Structural Equation Modeling of Causal Effects in Linear Regression Models

In the presence of omitted variables or similar validity threats, regression estimates are biased. Unbiased estimates (the causal effects) can be obtained in large samples by fitting instead the Instrumental Variables Regression (IVR) model. The IVR model can be estimated using structural equation modeling (SEM) software or using Econometric estimators such as two-stage least squares (2SLS). We describe 2SLS using SEM terminology, and report a simulation study in which we generated data according to a regression model in the presence of omitted variables and fitted (a) a regression model using ordinary least squares, (b) an IVR model using maximum likelihood (ML) as implemented in SEM software, and (c) an IVR model using 2SLS. Coverage rates of the causal effect using regression methods are always unacceptably low (often 0). When using the IVR model, accurate coverage is obtained across all conditions when N = 500. Even when the IVR model is misspecified, better coverage than regression is generally obtained. Differences between 2SLS and ML are small and favor 2SLS in small samples (N ≤ 100).     

类比推理的形式化探析

类比推理是一种重要的推理,仅仅由属性的相似性去解释类比是不够的,应该从形式上对类比推理进行深入的探究.类比是源域S和目标域T之间的结构保持映射fM.在类比推理的过程中,对源域S的选择、对结构保持映射的要求以及对类比迁移的控制,都会影响类比推理的关联性和可靠性.     

基于贝叶斯估计的Context量化器设计方法

介绍了一种基于贝叶斯估计的Context量化器设计方法.通过将自适应码长增量与贝叶斯估计中的先验概率估计进行结合,使得该量化器在不需要先验知识的情况下,既考虑到Context量化本身的特点,又使得编码后信源的自适应码长最短,最终保证了Context量化的自适应性.实验结果表明,该Context量化器设计方法能获得最优量化结果,达到设计目标.     

基于贝叶斯网络的组织创新影响机制研究

组织创新影响机制中各变量具有不确定性与动态性等特点,可以尝试进行贝叶斯网络分析。根据贝叶斯网络的基本原理,构建了基于贝叶斯网络的组织创新影响机制模型,并对复杂贝叶斯网络计算问题的简化问题进行了探讨。实例应用表明,该方法克服了其他传统分析方法局限于线性、静态分析的缺点,较为准确地反映了组织创新影响机制各变量间的动态关系。     

Bayesian reasoning in avalanche terrain: a theoretical investigation

In this article, I explore a Bayesian approach to avalanche decision-making. I motivate this perspective by highlighting a version of the base-rate fallacy and show that a similar pattern applies to decision-making in avalanche-terrain. I then draw out three theoretical lessons from adopting a Bayesian approach and discuss these lessons critically. Lastly, I highlight a number of challenges for avalanche educators when incorporating the Bayesian perspective in their curriculum.     

MUST的用法

本文论述ｍｕｓｔ的不同用法,对其否定形式、推测含义及名词功能进行分析。     

含意理论在小说翻译中的应用

含意在文学作品中是极为常见的现象。小说作为文学作品的重要表现形式,基本上遵循一般的语言规范,但也经常利用独特的句法、词法手段增加作品的含意性。因此,翻译时妥善处理含意问题,才能真正译出原文的意义。本文以《名利场》原作及杨必中译本为例,对比分析了英汉两种语言的常规性以及它们在使用中各自产生的特性,认为含意理论中的"含意推导"和"合作原则"可用来指导译者正确理解原文并在翻译中恰如其分地表述出来,使译文尽量与原文的意义达成真正对等。     

基于专家系统设计过程的盲目搜索技术优化策略的探讨

分析了盲目搜索技术的特点、算法、搜索过程、时间复杂度和空间复杂度,并在此基础上讨论了盲目搜索技术面向专家系统的设计过程所存在的缺点。依据专家系统知识表示和推理过程的特点,提出了一种改进的搜索方法——跳棋法,并阐述了跳棋法搜索策略的基本思想和算法过程,描述了这种搜索方式更适用于专家系统设计过程的原因。