共查询到19条相似文献，搜索用时 265 毫秒
1.
为了分析交通流的速度不均匀性,提出了一种将聚类分析和概率分布函数拟合相结合的新方法.首先,为了确定最优的子类数,采用两步聚类法对实际的速度数据进行聚类分析,分析表明将速度数据分为2类最能反映交通流的固有类型.然后,将此信息用于指导概率分布函数拟合,采用正态分布、偏正态分布和偏-T分布函数分别拟合各子类数据的概率分布,发现偏-T分布函数拟合精度最高,偏正态分布次之,正态分布最差.模型分析结果表明,所提出的混合分布模型比传统单个分布模型具有更好的拟合能力和通用性.此外,新方法在数据拟合方面更加灵活,且能提供更精确的速度分布模型曲线. 相似文献
2.
中国大学生英语能力目标界定､大学英语教学和大学生自主学习是与大学英语校本考试设计､实施密切相关的三个重要方面｡ 朱正才等(2021)已经讨论了与英语能力目标构念相关的6 个问题,本文作为其姊妹篇则主要讨论与大学英语教学和校本英语水平考试有关的6个问题,包括教学与测试的关系､分级教学､中国大学生英语能力现状与对策､自主学习以及校本英语水平考试的效度与信度｡对这些问题的看法和态度会严重影响校本考试的设计思路和价值取向,以至于会进一步影响到整个大学英语教学系统的有效性和工作效率｡ 相似文献
3.
采用带有随机微分方程的非线性混合效应模型对群体药物代谢动力学数据建模,通过在状态方程中引入随机项,将常微分方程扩展到随机微分方程.和常微分方程相比,随机微分方程可解决群体药物代谢动力学模型中相关残差问题.利用贝叶斯估计对非线性混合效应随机微分方程模型参数进行估计,给出群体参数及个体参数的精确后验分布,将Gibbs和Metropolis-Hastings算法相结合,给出参数估计值.通过计算机模拟和实例分析验证了方法的可靠性,结果表明利用非线性混合效应随机微分方程模型及贝叶斯估计方法分析群体药物代谢动力学数据是可行的. 相似文献
4.
处理一般事件及极端事件风险的混合分布 总被引：1，自引：0，他引：1
以指数分布为例，提出用混合分布去拟合一组数据，比用单一的指数分 要好，一般的原始分布只对中间部分拟合较好，而对尾部拟合较差，可用混合分布来克服此缺陷。 相似文献
5.
文章使用GDINA R程序包,借助Wald检验为英语听力诊断试题中的多属性题目选出最优简约模型,组成混合模型(Mixed-CDMs)。基于Mixed-CDMs与G-DINA模型的对比分析,文章发现:在样本量不够大(N=726)的情况下,Mixed-CDMs满足模型-数据绝对拟合的较高要求,相对拟合性、人员拟合性、属性分类的可靠性以及参数估计的准确性都有所提高,且属性之间的关系更加直观易读。由此,文章验证了混合模型对于英语听力诊断测评具有适用性并有一定的应用优势,这为混合模型在英语听力测试中的应用提供了实证依据,有助于加深对英语听力认知属性关系的了解,并可为其它语言测试使用混合模型提供借鉴。 相似文献
6.
《考试研究》2015,(4)
传统的项目反应理论模型由于不能很好地处理非连续数据而影响了对具有潜在类别属性的特质进行精确估计。混合项目反应理论不仅能够精确地估计项目参数和能力参数,而且可以实现按照不同类别属性的潜在特质与行为对被试进行自动鉴别。随着研究的发展,混合分部评分模型、混合Logistic模型、多水平项目反应理论模型以及带协变量的混合项目反应理论模型等相继诞生,并在教育测验分析与编制、项目功能差异分析以及其他拓展性实践应用中展现出优良的品质。开发多维混合项目反应理论模型、多维混合认知诊断模型以及混合题组模型等并对其进行本土化研究与应用将是混合项目反应理论的一大研究热点与方向。 相似文献
7.
针对数据真实的概率分布不符合事先假设的高斯混合模型的情形,提出了一种鲁棒的基于高斯混合模型的聚类方法.首先,提出了一种新的模型选择准则,即完整似然最短信息长度准则.该准则不仅能衡量模型对数据的拟合优度,还能度量该模型对数据分组的性能.然后,将该准则作为聚类的代价函数,提出了一种新的期望最大化算法来估计模型参数.与标准的期望最大化算法相比,新算法能较好地避免不理想的局部最优解.实验结果表明:当数据概率分布模型不符合假设的高斯混合模型时,所提方法可克服现有的基于高斯混合模型聚类方法过拟合的缺点,鲁棒地得到准确的聚类结果. 相似文献
8.
口译活动是发言人､译员等在社会情境中的多模态亲历过程｡口译教学可借助贴真建模思路,从参与者､活动和系统三个视角对口译活动录像与情境化影视片段进行观察分析､数据提取,构建可触发学生贴真体验的多模态口译教学语料库｡教师利用语料库讲解口译现象､评估口译质量,要求学生模拟该口译任务进行练习,激发学生多种感官模态对情境化口译活动进行贴真体验｡今后还可研发口译虚拟仿真实验教学,从而提升学生的情境口译能力｡ 相似文献
9.
无论是在自然科学领域还是在人文社会科学领域,我们会遇到各种各样的计数数据.对于社会生活、生产、管理中的一些计数数据通常是用泊松分布以及泊松过程来描述具有非常好的拟合效果.然而实际环境中,由于受各种因素影响与制约,出现了很多频数为零的数据.此时对含零特别多的计数数据,我们仍用泊松回归模型拟合就有些不合适了[1],因此人们开始构造新的模型,其中对于这种数据拟合效果比较好的一种模型就是零过多泊松分布模型(Zero-Inflated Poisson Distribution)[3,5]. 相似文献
10.
赵晓兵 《雁北师范学院学报》2006,22(2):1-3,6
混合和非混合模型被广泛地用于拟合含有持久生存者的生存数据．当添加有协变量时,这两类模型却都不具有比例危险率(PH)结构．“不正确”比例危险率模型能克服上述缺点．本文研究此模型的识别问题．同时也考虑“不正确”的可加危险率(AH)模型的识别问题．本文的结果对模型的参数估计有重要意义． 相似文献
11.
Nonlinear models are effective tools for the analysis of longitudinal data. These models provide a flexible means for describing data that follow complex forms of change. Exponential and logistic functions that include a parameter to represent an asymptote, for instance, are useful for describing responses that tend to level off with time. There are forms of nonlinear latent curve models and nonlinear mixed-effects model that are equivalent, and so given the same set of data, growth function, distributional assumptions, and method of estimation, the 2 models yield equivalent results. There are also forms that are strikingly different and can yield different interpretations for a given set of data. This article discusses cases in which nonlinear mixed-effects models and nonlinear latent curve models are equivalent and those in which they are different and clarifies the estimation needs of the different models. Examples based on empirical data help to illustrate these points. 相似文献
12.
A Conditional Joint Modeling Approach for Locally Dependent Item Responses and Response Times 
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下载免费PDF全文 The assumption of conditional independence between the responses and the response times (RTs) for a given person is common in RT modeling. However, when the speed of a test taker is not constant, this assumption will be violated. In this article we propose a conditional joint model for item responses and RTs, which incorporates a covariance structure to explain the local dependency between speed and accuracy. To obtain information about the population of test takers, the new model was embedded in the hierarchical framework proposed by van der Linden ( 2007 ). A fully Bayesian approach using a straightforward Markov chain Monte Carlo (MCMC) sampler was developed to estimate all parameters in the model. The deviance information criterion (DIC) and the Bayes factor (BF) were employed to compare the goodness of fit between the models with two different parameter structures. The Bayesian residual analysis method was also employed to evaluate the fit of the RT model. Based on the simulations, we conclude that (1) the new model noticeably improves the parameter recovery for both the item parameters and the examinees’ latent traits when the assumptions of conditional independence between the item responses and the RTs are relaxed and (2) the proposed MCMC sampler adequately estimates the model parameters. The applicability of our approach is illustrated with an empirical example, and the model fit indices indicated a preference for the new model. 相似文献
13.
Linear factor analysis (FA) models can be reliably tested using test statistics based on residual covariances. We show that the same statistics can be used to reliably test the fit of item response theory (IRT) models for ordinal data (under some conditions). Hence, the fit of an FA model and of an IRT model to the same data set can now be compared. When applied to a binary data set, our experience suggests that IRT and FA models yield similar fits. However, when the data are polytomous ordinal, IRT models yield a better fit because they involve a higher number of parameters. But when fit is assessed using the root mean square error of approximation (RMSEA), similar fits are obtained again. We explain why. These test statistics have little power to distinguish between FA and IRT models; they are unable to detect that linear FA is misspecified when applied to ordinal data generated under an IRT model. 相似文献
14.
Model fit indices are being increasingly recommended and used to select the number of factors in an exploratory factor analysis. Growing evidence suggests that the recommended cutoff values for common model fit indices are not appropriate for use in an exploratory factor analysis context. A particularly prominent problem in scale evaluation is the ubiquity of correlated residuals and imperfect model specification. Our research focuses on a scale evaluation context and the performance of four standard model fit indices: root mean square error of approximate (RMSEA), standardized root mean square residual (SRMR), comparative fit index (CFI), and Tucker–Lewis index (TLI), and two equivalence test-based model fit indices: RMSEAt and CFIt. We use Monte Carlo simulation to generate and analyze data based on a substantive example using the positive and negative affective schedule (N = 1,000). We systematically vary the number and magnitude of correlated residuals as well as nonspecific misspecification, to evaluate the impact on model fit indices in fitting a two-factor exploratory factor analysis. Our results show that all fit indices, except SRMR, are overly sensitive to correlated residuals and nonspecific error, resulting in solutions that are overfactored. SRMR performed well, consistently selecting the correct number of factors; however, previous research suggests it does not perform well with categorical data. In general, we do not recommend using model fit indices to select number of factors in a scale evaluation framework. 相似文献
15.
The purpose of the present study was to validate an existing school environment instrument, the School Level Environment Questionnaire
(SLEQ). The SLEQ consists of 56 items, with seven items in each of eight scales. One thousand, one hundred and six (1106)
teachers in 59 elementary schools in a southwestern USA public school district completed the instrument. An exploratory factor
analysis was undertaken for a random sample of half of the completed surveys. Using principal axis factoring with oblique
rotation, this analysis suggested that 13 items should be dropped and that the remaining 43 items could best be represented
by seven rather than eight factors. A confirmatory factor analysis was run with the other half of the original sample using
structural equation modeling. Examination of the fit indices indicated that the model came close to fitting the data, with
goodness-of-fit (GOF) coefficients just below recommended levels. A second model was then run with two of the seven factors,
with their associated items removed. That left five factors with 35 items. Model fit was improved. A third model was tried,
using the same five factors with 35 items but with correlated residuals between some of the items within a factor. This model
seemed to fit the data well, with GOF coefficients in recommended ranges. These results led to a refined, more parsimonious
version of the SLEQ that was then used in a larger study. Future research is needed to see if this model would fit other samples
in different elementary schools and in secondary schools both in the USA and in other countries.
This revised version was published online in July 2006 with corrections to the Cover Date. 相似文献
16.
Users assume statistical software packages produce accurate results. In this article, the authors systematically examined Statistical Package for the Social Sciences (SPSS) and Statistical Analysis System (SAS) for 3 analysis of variance (ANOVA) designs, mixed-effects ANOVA, fixed-effects analysis of covariance (ANCOVA), and nested ANOVA. For each model, the authors examined 3 different data sets. With the mixed-effects design, results were always correct for SPSS syntax and SAS syntax. For SPSS point-and-click, the F and p values for the random-effect were always incorrect as the wrong error term is used. With the ANCOVA design, results varied both by software package and by type of sums of squares. With the nested design, the p values for the F and multiple comparison procedure did not agree for the nonnested factor in SPSS point-and-click. Recommendations were made regarding which package to use for each design. 相似文献
17.
Drawing valid inferences from modern measurement models is contingent upon a good fit of the data to the model. Violations of model‐data fit have numerous consequences, limiting the usefulness and applicability of the model. As Bayesian estimation is becoming more common, understanding the Bayesian approaches for evaluating model‐data fit models is critical. In this instructional module, Allison Ames and Aaron Myers provide an overview of Posterior Predictive Model Checking (PPMC), the most common Bayesian model‐data fit approach. Specifically, they review the conceptual foundation of Bayesian inference as well as PPMC and walk through the computational steps of PPMC using real‐life data examples from simple linear regression and item response theory analysis. They provide guidance for how to interpret PPMC results and discuss how to implement PPMC for other model(s) and data. The digital module contains sample data, SAS code, diagnostic quiz questions, data‐based activities, curated resources, and a glossary. 相似文献
18.
Gabriela Stegmann Ross Jacobucci Jeffrey R. Harring Kevin J. Grimm 《Structural equation modeling》2018,25(1):160-165
In this software review, we provide a brief overview of four R functions to estimate nonlinear mixed-effects programs: nlme (linear and nonlinear mixed-effects model), nlmer (from the lme4 package, linear mixed-effects models using Eigen and S4), saemix (stochastic approximation expectation maximization), and brms (Bayesian regression models using Stan). We briefly describe the approaches used, provide a sample code, and highlight strengths and weaknesses of each. 相似文献
19.
Alberto Maydeu-Olivares Amanda J. Fairchild Alexander G. Hall 《Structural equation modeling》2017,24(4):495-505
The power of the chi-square test statistic used in structural equation modeling decreases as the absolute value of excess kurtosis of the observed data increases. Excess kurtosis is more likely the smaller the number of item response categories. As a result, fit is likely to improve as the number of item response categories decreases, regardless of the true underlying factor structure or χ2-based fit index used to examine model fit. Equivalently, given a target value of approximate fit (e.g., root mean square error of approximation ≤ .05) a model with more factors is needed to reach it as the number of categories increases. This is true regardless of whether the data are treated as continuous (common factor analysis) or as discrete (ordinal factor analysis). We recommend using a large number of response alternatives (≥ 5) to increase the power to detect incorrect substantive models. 相似文献