共查询到20条相似文献，搜索用时 31 毫秒
1.
Statistical theories of goodness-of-fit tests in structural equation modeling are based on asymptotic distributions of test statistics. When the model includes a large number of variables or the population is not from a multivariate normal distribution, the asymptotic distributions do not approximate the distribution of the test statistics very well at small sample sizes. A variety of methods have been developed to improve the accuracy of hypothesis testing at small sample sizes. However, all these methods have their limitations, specially for nonnormal distributed data. We propose a Monte Carlo test that is able to control Type I error with more accuracy compared to existing approaches in both normal and nonnormally distributed data at small sample sizes. Extensive simulation studies show that the suggested Monte Carlo test has a more accurate observed significance level as compared to other tests with a reasonable power to reject misspecified models. 相似文献
2.
Morten Moshagen 《Structural equation modeling》2013,20(1):86-98
The size of a model has been shown to critically affect the goodness of approximation of the model fit statistic T to the asymptotic chi-square distribution in finite samples. It is not clear, however, whether this “model size effect” is a function of the number of manifest variables, the number of free parameters, or both. It is demonstrated by means of 2 Monte Carlo computer simulation studies that neither the number of free parameters to be estimated nor the model degrees of freedom systematically affect the T statistic when the number of manifest variables is held constant. Increasing the number of manifest variables, however, is associated with a severe bias. These results imply that model fit drastically depends on the size of the covariance matrix and that future studies involving goodness-of-fit statistics should always consider the number of manifest variables, but can safely neglect the influence of particular model specifications. 相似文献
3.
The authors compared the Type I error rate and the power to detect differences in slopes and additive treatment effects of analysis of covariance (ANCOVA) and randomized block (RB) designs with a Monte Carlo simulation. For testing differences in slopes, 3 methods were compared: the test of slopes from ANCOVA, the omnibus Block × Treatment interaction, and the linear component of the Block × Treatment interaction of RB. In the test for adjusted means, 2 variations of both ANCOVA and RB were used. The power of the omnibus test of the interaction decreased dramatically as the number of blocks used increased and was always considerably smaller than the specific test of differences in slopes found in ANCOVA. Tests for means when there were concomitant differences in slopes showed that only ANCOVA uniformly controlled Type I error under all configurations of design variables. The most powerful option in almost all simulations for tests of both slopes and means was ANCOVA. 相似文献
4.
《Structural equation modeling》2013,20(3):325-352
This study examined the effects of ignoring multilevel data structures in nonhierarchical covariance modeling using a Monte Carlo simulation. Multilevel sample data were generated with respect to 3 design factors: (a) intraclass correlation, (b) group and member configuration, and (c) the models that underlie the between-group and within-group variance components associated with multilevel data. Covariance models that ignored the multilevel structure were then fit to the data. Results indicated that when variables exhibit minimal levels of intraclass correlation, the chi-square model/data fit statistic, the parameter estimators, and the standard error estimators are relatively unbiased. However, as the level of intraclass correlation increases, the chi-square statistic, the parameters, and their standard errors all exhibit estimation problems. The specific group/member configurations as well as the underlying between-group and within-group model structures further exacerbate the estimation problems encountered in the nonhierarchical analysis of multilevel data. 相似文献
5.
考虑到路面性能预测中存在的大量可变性和不确定性,为使得路面性能PSI的预测结果可信且有意义,提出了一种基于分布的路面性能指标PSI预测方法.该方法建立在AASHTO路面性能预测模型基础上,把预测变量处理成具有某种概率分布的随机变量,通过蒙特卡洛数值模拟获得PSI的概率分布.基于路面结构和交通参数建立仿真模型,应用PERFORM程序得到数值计算结果.研究结果表明:交通荷载、表面层材料特性和路面初始性能是影响未来路面性能的最主要因素.在获得路面性能PSI指标的概率分布后,其他统计量如未来路面性能的均值函数和方差函数可以很容易得到. 相似文献
6.
This Monte Carlo simulation adds to the growing body of enumeration index performance research in continuous response variable mixture models by addressing the issue of the performance of these indexes in discrete-time survival mixture analysis (DTSMA) models. Results showed that although all enumeration indexes performed very well in identifying a homogeneous DTSMA model (i.e., k = 1 hazard function in the sample data), the findings also showed that the enumeration indexes performed poorly in identifying the correct number of unobserved hazard functions present in a heterogeneous (i.e., k = 3) DTSMA model. More important, the performance of the enumeration indexes for the heterogeneous DTSMA models did not improve as the sample size, the effect of time-invariant covariates, or adjacent hazard function separation distance increased, which is inconsistent with some previous Monte Carlo simulation results. The limitations of this Monte Carlo simulation study and future empirical investigation possibilities are both discussed. 相似文献
7.
《Journal of Experimental Education》2012,80(4):188-191
This study was designed to determine the minimum number of points required for continuous scaled variables before the Pearson product moment correlation coefficient (PPMCC) ceases to be an accurate estimate of their original correlation coefficient. The study was performed on samples from normal, exponential, and uniform distributions by using Monte Carlo techniques. After the PPMCC was determined for each sample, multiple grouping procedures were applied and the PPMCCs were recalculated to determine the effect of scaling on the correlation coefficient. The results showed that the PPMCC obtained by using transformed discrete ordinal-level variables tended to underestimate the true parameter. The minimum number required for precise PPMCC is five and the use of PPMCC with five or more points is recommended. 相似文献
8.
改进粒子滤波在被动目标跟踪中的应用 总被引：3，自引：0，他引：3
As a new method for dealing with any nonlinear or non-Ganssian distributions, based on the Monte Carlo methods and Bayesian filtering, particle filters （PF） are favored by researchers and widely applied in many fields. Based on particle filtering, an improved extended Kalman filter （EKF） proposal distribution is presented. Evaluation of the weights is simplified and other improved techniques including the residual resampling step and Markov Chain Monte Carlo method are introduced for target tracking. Performances of the EKF, basic PF and the improved PF are compared in target tracking examples. The simulation results confirm that the improved particle filter outperforms the others. 相似文献
9.
利用蒙特卡罗算法对伪随机信号分布进行采样处理,依据伪随机信号的概率函数,建立了伪随机信号分析模型,同时给出了误差效果评判方法,可以依据精度要求而选择不同的采样数目。仿真结果中证实了该方法的有效性。 相似文献
10.
The latent state–trait (LST) theory is an extension of the classical test theory that allows one to decompose a test score into a true trait, a true state residual, and an error component. For practical applications, the variances of these latent variables may be estimated with standard methods of structural equation modeling (SEM). These estimates allow one to decompose the coefficient of reliability into a coefficient of consistency (indicating true effects of the person) plus a coefficient of occasion specificity (indicating true effects of the situation and the person–situation interaction). One disadvantage of this approach is that the standard SEM analysis requires large sample sizes. This article aims to overcome this disadvantage by presenting a simple method that allows one to estimate the LST parameters algebraically from the observed covariance matrix. A Monte Carlo simulation suggests that the proposed method may be superior to the standard SEM analysis in small samples. 相似文献
11.
《天津大学学报(英文版)》2017,(4)
Effectively and accurately modelling the spatial relation of fracture surfaces is crucial in the design and construction of large hydropower dams having a complex underlying geology. However, fracture surfaces are randomly formed and vary greatly with respect to their spatial distribution, which makes the construction of accurate 3-D models challenging. In this study, we use an optimal Monte Carlo simulation and dynamic conditioning to construct a fracture network model. We found the optimal Monte Carlo simulation to effectively reduce the error associated with the Monte Carlo method and use dynamic conditioning to ensure the consistency of the model with the actual distribution of fractures on the excavation faces and outcrops. We applied this novel approach to a hydropower station on the Jinshajiang River, China. The simulation results matched the real sampled values well, confirming that the model is capable of effectively and accurately simulating the spatial relations in a fracture network. 相似文献
12.
《Structural equation modeling》2013,20(3):378-396
Recently, analysis of structural equation models with polytomous and continuous variables has received a lot of attention. However, contributions to the selection of good models are limited. The main objective of this article is to investigate the maximum likelihood estimation of unknown parameters in a general LISREL-type model with mixed polytomous and continuous data and propose a model selection procedure for obtaining good models for the underlying substantive theory. The maximum likelihood estimate is obtained by a Monte Carlo Expectation Maximization algorithm, in which the E step is evaluated via the Gibbs sampler and the M step is completed via the method of conditional maximization. The convergence of the Monte Carlo Expectation Maximization algorithm is monitored by the bridge sampling. A model selection procedure based on Bayes factor and Occam's window search strategy is proposed. The effectiveness of the procedure in accounting for the model uncertainty and in picking good models is discussed. The proposed methodology is illustrated with a real example. 相似文献
13.
One challenge in mediation analysis is to generate a confidence interval (CI) with high coverage and power that maintains a nominal significance level for any well-defined function of indirect and direct effects in the general context of structural equation modeling (SEM). This study discusses a proposed Monte Carlo extension that finds the CIs for any well-defined function of the coefficients of SEM such as the product of k coefficients and the ratio of the contrasts of indirect effects, using the Monte Carlo method. Finally, we conduct a small-scale simulation study to compare CIs produced by the Monte Carlo, nonparametric bootstrap, and asymptotic-delta methods. Based on our simulation study, we recommend researchers use the Monte Carlo method to test a complex function of indirect effects. 相似文献
14.
This paper deals with the statistical modeling of latent topic hierarchies in text corpora. The height of the topic tree is assumed as fixed, while the number of topics on each level as unknown a priori and to be inferred from data. Taking a nonparametric Bayesian approach to this problem, we propose a new probabilistic generative model based on the nested hierarchical Dirichlet process （nHDP） and present a Markov chain Monte Carlo sampling algorithm for the inference of the topic tree structure as well as the word distribution of each topic and topic distribution of each document. Our theoretical analysis and experiment results show that this model can produce a more compact hierarchical topic structure and captures more fine-grained topic relationships compared to the hierarchical latent Dirichlet allocation model. 相似文献
15.
考虑了多元t-copula的上尾象限相依指数和上尾极值相依指数,该t-copula是在相依结构下定义的．由于多元连续型随机变量的copula函数关于严格单调递增变换具有不变性质,由此推导了多元t-copula的尾相依指数的精确表达式,得到的结果明显比以往文献给出的结论更加简洁．然后,讨论了这2个相依指数关于相关系数的局部单调性质：上尾极值相依指数关于相关系数是严格单调递增的,但上尾象限相依指数的单调性比较复杂．通过蒙特卡罗模拟数据验证了结果的正确性．同时,发现所有结论可以推广到生成随机变量是正则变化的分布类中． 相似文献
16.
《Structural equation modeling》2013,20(4):587-614
The recovery of weak factors has been extensively studied in the context of exploratory factor analysis. This article presents the results of a Monte Carlo simulation study of recovery of weak factor loadings in confirmatory factor analysis under conditions of estimation method (maximum likelihood vs. unweighted least squares), sample size, loading size, factor correlation, and model specification (correct vs. incorrect). The effects of these variables on goodness of fit and convergence are also examined. Results show that recovery of weak factor loadings, goodness of fit, and convergence are improved when factors are correlated and models are correctly specified. Additionally, unweighted least squares produces more convergent solutions and successfully recovers the weak factor loadings in some instances where maximum likelihood fails. The implications of these findings are discussed and compared to previous research. 相似文献
17.
Maximum likelihood is commonly used for estimation of model parameters in analysis of two-level structural equation models. Constraints on model parameters could be encountered in some situations such as equal factor loadings for different factors. Linear constraints are the most common ones and they are relatively easy to handle in maximum likelihood analysis. Nonlinear constraints could be encountered in complicated applications. In this paper we develop an EM-type algorithm for estimating model parameters with both linear and nonlinear constraints. The empirical performance of the algorithm is demonstrated by a Monte Carlo study. Application of the algorithm for linear constraints is illustrated by setting up a two-level mean and covariance structure model for a real two-level data set and running an EQS program. 相似文献
18.
莫文辉 《十堰职业技术学院学报》2013,26(1):97-99
随机因素对振动的影响不可忽略。本文将质量刚度、阻尼系数看作随机变量,研究了单自由度强迫振动的共振频率的可靠性。对随机变量进行计算机模拟,产生大量样本。应用Monte Carlo模拟研究了振动位移的可靠性。模拟次数越多,求得的可靠度精度越高。算例表明本文方法是正确的。 相似文献
19.
Clement A. Stone 《Journal of Educational Measurement》2000,37(1):58-75
Assessing the correspondence between model predictions and observed data is a recommended procedure for justifying the application of an IRT model. However, with shorter tests, current goodness-of-fit procedures that assume precise point estimates of ability, are inappropriate. The present paper describes a goodness-of-fit statistic that considers the imprecision with which ability is estimated and involves constructing item fit tables based on each examinee's posterior distribution of ability, given the likelihood of their response pattern and an assumed marginal ability distribution. However, the posterior expectations that are computed are dependent and the distribution of the goodness-of-fit statistic is unknown. The present paper also describes a Monte Carlo resampling procedure that can be used to assess the significance of the fit statistic and compares this method with a previously used method. The results indicate that the method described herein is an effective and reasonably simple procedure for assessing the validity of applying IRT models when ability estimates are imprecise. 相似文献
20.
We consider a multivariate generalized latent variable model to investigate the effects of observable and latent explanatory variables on multiple responses of interest. Various types of correlated responses, such as continuous, count, ordinal, and nominal variables, are considered in the regression. A generalized confirmatory factor analysis model that is capable of managing mixed-type data is proposed to characterize latent variables via correlated observed indicators. In addressing the complicated structure of the proposed model, we introduce continuous underlying measurements to provide a unified model framework for mixed-type data. We develop a multivariate version of the Bayesian adaptive least absolute shrinkage and selection operator procedure, which is implemented with a Markov chain Monte Carlo (MCMC) algorithm in a full Bayesian context, to simultaneously conduct estimation and model selection. The empirical performance of the proposed methodology is demonstrated through a simulation study. An application of the proposed method to a study of adolescent substance abuse based on the National Longitudinal Survey of Youth is presented. 相似文献