电子电路的电路模型解析

从理想模型出发，描述了理想模型及在科学研究中的重要作用。闸述了在电子电路中应用电路模型的方法分析电路的必要性，同时，对电子电路中电路模型进行剖析，得出实际电路元器件集中参数模型、集中参数模型的适用范围以及集中参数模型在分析和解决实际电路的重要指导作用。     

面向对象的CAD系统模型

本系统地分析了以数据为中心，以执行为中心和面向对象的CAD系统模型的构成及其优缺点，在此基础上，运用基于组件的软件工程技术，提出了一种结构层次清晰，各模块接口规范，联系简洁，易于扩充的两级总线模型，并详细地分析了该模型中各组成部件的功能和联系方法，最后给出模型的应用实例和结论。     

论宏观经济计量模型的发展

自丁伯根（1937）开创宏观经济计量模型的先河开始，宏观经济计量模型经历了30、40年代的最初发展，50、60年代的蓬勃发展乃至70年代的衰落，到后来宏观经济计量模型各种理论和应用不断完善的发展过程。本通过介绍宏观经济模型的发展，并结合比较80年代出现的VAR模型和CGE模型，指出经典经济计量模型仍然是目前各国政府和商业部门广泛使用的经济分析和预测工具。     

环境经济系统SD模型的建立

利用系统动力学对环境经济系统进行研究。对镇江市而言，系统动力学 模型分成三块：工业子模型、人口子模型和环境污染子模型。三个子模型相互联系、相互影响、相互制约。以工业子模型为例说明了SD模型的建立。通过分析，绘制了工业子模型的因果反馈图，并进行了流图分析，据此采用系统动力学专用语言DYNAO语言，编写工业子模型仿真程序。最后对模型进行了有效性验证。     

简单的垮坝洪水预报模塑

人们对降雨径流洪水预报的研究较多，研制了不少流域模型。如新安江模型、陕北模型和国外研制的萨克拉门托模型、水箱模型等。不少地区已找到了适合当地的模型井率定出了模型在当地应用的参数值，具有了一定的经验。数十年来，人们为了防洪、灌溉、发电等目的，在全国已建设了不少大、中、小型水库。     

制导武器的火力分配模型

针对制导武器的特点和火力分配研究中的问题，提出了一种新的火力分配模型，以巡航导弹对敌编队进行攻击为例，得出了运用该模型的导弹火力分配方案。该模型在思路上区别于其它的火力分配模型，对于合理进行攻击型导弹武器的战术运用，具有实际的指导意义。     

基于Matlab的DLA模型的计算机模拟

该文概述了分形理论的产生和DLA有限凝聚模型，分析了DLA模型的计算机模拟方法，并对模型进行了演变讨论，同时给出了模拟效果图。     

基于PHP/MySQL的古汉语语料库系统的设计与实现

结合古汉语语料库系统数据结构的基本模型，根据系统的B／S模型的设计思路，分析了B／S结构和PHP技术特点，提出了古汉语语料库系统的一般模型，并用PHP＋MySQL技术实现了系统的主要功能。     

区域地质旅游资源的评价方法

基于地质旅游资源的特征以及分类情况，建立了地质旅游资源的评价模型（资源个体价值评价模型和资源外部开发条件评价模型），在评价模型的基础上，提出了分布一综合分析法，即对资源个体价值和资源的外部开发条件进行了先分步后综合的评价；建立了地质旅游资源的评价因子体系和评价指标体系，拟定了资源个体价值评价计算公式、外部开发条件评价计算公式和综合分值的计算公式，形成了一套科学的、严谨的评价体系。     

资产净持有可控的证券组合投资决策模型研究

以单因素和多因素模型及CAPM和APT理论为基础，分别提出了资产净持有可控的单因素和多因素证券组合投资决策模型，通过最小风险的分析，又提出了更简单的模型。     

Handling Missing Data in the Modeling of Intensive Longitudinal Data

Myriad approaches for handling missing data exist in the literature. However, few studies have investigated the tenability and utility of these approaches when used with intensive longitudinal data. In this study, we compare and illustrate two multiple imputation (MI) approaches for coping with missingness in fitting multivariate time-series models under different missing data mechanisms. They include a full MI approach, in which all dependent variables and covariates are imputed simultaneously, and a partial MI approach, in which missing covariates are imputed with MI, whereas missingness in the dependent variables is handled via full information maximum likelihood estimation. We found that under correctly specified models, partial MI produces the best overall estimation results. We discuss the strengths and limitations of the two MI approaches, and demonstrate their use with an empirical data set in which children’s influences on parental conflicts are modeled as covariates over the course of 15 days (Schermerhorn, Chow, & Cummings, 2010).     

Models for Semiordered Data to Address Not Applicable Responses in Scale Measurement

The purpose of this study is to develop and evaluate unidimensional models that can handle semiordered data within scale items (i.e., items with multiple ordered response categories, and one additional nominal response category). We apply the models to scale data with not applicable (NA) responses to compare the model performance to conditions in which NA responses are treated as missing and ignored. We also conduct a small simulation study based on the operational study to evaluate the parameter recovery of the models under the operational conditions. Findings indicate that the proposed models show promise for (a) reducing standard errors of trait estimates for persons who select NA responses, (b) reducing nonresponse bias in trait estimates for persons who select NA responses, and (c) providing substantive information to practitioners about the nature of the relationship between NA selection and the trait of measurement.     

Assessing Mediation in Dyadic Data Using the Actor-Partner Interdependence Model

The assessment of mediation in dyadic data is an important issue if researchers are to test process models. Using an extended version of the actor–partner interdependence model the estimation and testing of mediation is complex, especially when dyad members are distinguishable (e.g., heterosexual couples). We show how the complexity of the model can be reduced by assuming specific dyadic patterns. Using structural equation modeling, we demonstrate how specific mediating effects and contrasts among effects can be tested by phantom models that permit point and bootstrap interval estimates. We illustrate the assessment of mediation and the strategies to simplify the model using data from heterosexual couples.     

基于媒体报道数据的上海世博会影响力的定量分析

由权威媒体报道这一侧面,利用“中国资讯行-中国经济新闻库”提供的数据,从行业、时间和空间三个维度来分析上海世博会的影响力。文章以媒体报道的篇数、字数和延续时间作为衡量因子,设计出两种影响力指数模型,优势互补,为定量分析提供依据;运用影响力指数模型,解决了核心检索词的选定和数据使用的可靠性问题。     

Graphical Representation of Missing Data Problems

Rubin’s classic missingness mechanisms are central to handling missing data and minimizing biases that can arise due to missingness. However, the formulaic expressions that posit certain independencies among missing and observed data are difficult to grasp. As a result, applied researchers often rely on informal translations of these assumptions. We present a graphical representation of missing data mechanism, formalized in Mohan, Pearl, and Tian (2013). We show that graphical models provide a tool for comprehending, encoding, and communicating assumptions about the missingness process. Furthermore, we demonstrate on several examples how graph-theoretical criteria can determine if biases due to missing data might emerge in some estimates of interests and which auxiliary variables are needed to control for such biases, given assumptions about the missingness process.     

H-Likelihood Approach to Factor Analysis for Ordinal Data

Marginal likelihood-based methods are commonly used in factor analysis for ordinal data. To obtain the maximum marginal likelihood estimator, the full information maximum likelihood (FIML) estimator uses the (adaptive) Gauss–Hermite quadrature or stochastic approximation. However, the computational burden increases rapidly as the number of factors increases, which renders FIML impractical for large factor models. Another limitation of the marginal likelihood-based approach is that it does not allow inference on the factors. In this study, we propose a hierarchical likelihood approach using the Laplace approximation that remains computationally efficient in large models. We also proposed confidence intervals for factors, which maintains the level of confidence as the sample size increases. The simulation study shows that the proposed approach generally works well.     

Item Response Models for Multiple Attempts With Incomplete Data

Allowance for multiple chances to answer constructed response questions is a prevalent feature in computer‐based homework and exams. We consider the use of item response theory in the estimation of item characteristics and student ability when multiple attempts are allowed but no explicit penalty is deducted for extra tries. This is common practice in online formative assessments, where the number of attempts is often unlimited. In these environments, some students may not always answer‐until‐correct, but may rather terminate a response process after one or more incorrect tries. We contrast the cases of graded and sequential item response models, both unidimensional models which do not explicitly account for factors other than ability. These approaches differ not only in terms of log‐odds assumptions but, importantly, in terms of handling incomplete data. We explore the consequences of model misspecification through a simulation study and with four online homework data sets. Our results suggest that model selection is insensitive for complete data, but quite sensitive to whether missing responses are regarded as informative (of inability) or not (e.g., missing at random). Under realistic conditions, a sequential model with similar parametric degrees of freedom to a graded model can account for more response patterns and outperforms the latter in terms of model fit.     

Latent Differential Equation Models for Binary and Ordinal Data

This study evaluates latent differential equation models on binary and ordinal data. Binary and ordinal data are widely used in psychology research and many statistical models have been developed, such as the probit model and the logit model. We combine the latent differential equation model with the probit model through a threshold approach, and then compare the threshold model with a naive model, which blindly treats binary and ordinal data as continuous. Simulation results suggest that the naive model leads to bias on binary data and on ordinal data with fewer than 5 levels, whereas the threshold model is unbiased and efficient for binary and ordinal data. Two example analyses on empirical binary data and ordinal data show that the threshold model also has better external validity. The R code for the threshold model is provided.     

Brief Research Report: Growth Models With Small Samples and Missing Data

Small samples are common in growth models due to financial and logistical difficulties of following people longitudinally. For similar reasons, longitudinal studies often contain missing data. Though full information maximum likelihood (FIML) is popular to accommodate missing data, the limited number of studies in this area have found that FIML tends to perform poorly with small-sample growth models. This report demonstrates that the fault lies not with how FIML accommodates missingness but rather with maximum likelihood estimation itself. We discuss how the less popular restricted likelihood form of FIML, along with small-sample-appropriate methods, yields trustworthy estimates for growth models with small samples and missing data. That is, previously reported small sample issues with FIML are attributable to finite sample bias of maximum likelihood estimation not direct likelihood. Estimation issues pertinent to joint multiple imputation and predictive mean matching are also included and discussed.     

Growth Curve Models for Zero-Inflated Count Data: An Application to Smoking Behavior

This article applies growth curve models to longitudinal count data characterized by an excess of zero counts. We discuss a zero-inflated Poisson regression model for longitudinal data in which the impact of covariates on the initial counts and the rate of change in counts over time is the focus of inference. Basic growth curve models using a single outcome are described, as well as a model in which two linked outcomes constitute a dual-trajectory growth process. This model is applied to assess the impact of changes in financial stress on longitudinal change in smoking.