共查询到20条相似文献，搜索用时 15 毫秒
1.
Structural equation models have wide applications. One of the most important issues in analyzing structural equation models is model comparison. This article proposes a Bayesian model comparison statistic, namely the L ν-measure for both semiparametric and parametric structural equation models. For illustration purposes, we consider a Bayesian semiparametric approach for estimation and model comparison in the context of structural equation models with fixed covariates. A finite dimensional Dirichlet process is used to model the crucial latent variables, and a blocked Gibbs sampler is implemented for estimation. Empirical performance of the L ν-measure is evaluated through a simulation study. Results obtained indicate that the L ν-measure, which additionally requires very minor computational effort, gives satisfactory performance. Moreover, the methodologies are demonstrated through an example with a real data set on kidney disease. Finally, the application of the L ν-measure to Bayesian semiparametric nonlinear structural equation models is outlined. 相似文献
2.
George A. Marcoulides Adele Eskeles Gottfried Allen W. Gottfried Pamella H. Oliver 《Educational Research and Evaluation》2013,19(5):411-427
A longitudinal modeling approach was utilized to determine the existence of latent classes with regard to academic intrinsic motivation and the points of stability and transition of individuals between and within classes. A special type of latent Markov Chain model using Mplus was fit to data from the Fullerton Longitudinal Study, with academic intrinsic motivation measured from ages 9 through 17. Model fit using the Bayesian Information Criterion index was examined. The best fit involved a 3-class model, with classes designated as motivationally gifted, intermediate, and at-risk. Transitions between classes occurred mainly during childhood, whereas stability ensued by age 13. Methodological contributions are discussed, as well as theory and applications of the findings with regard to academic intrinsic motivation. 相似文献
3.
介绍了一种基于多重隐马尔克夫模型(Multiple HMM——MHMM)的手写体汉字识别新方法。该方法首先提取基于区域投影变换形成的边界链码特征,对每个汉字建立4个HMM,通过等比重综合方法将4个分类器的计算结果进行综合,从而得到识别结果。实验结果证明该方法较传统的HMM具有更高的抗干扰能力和更高的识别率。 相似文献
4.
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. 相似文献
5.
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
6.
This article examines the problem of specification error in 2 models for categorical latent variables; the latent class model and the latent Markov model. Specification error in the latent class model focuses on the impact of incorrectly specifying the number of latent classes of the categorical latent variable on measures of model adequacy as well as sample reallocation to latent classes. The results show that the clarity of remaining latent classes, as measured by the entropy statistic depends on the number of observations in the omitted latent class—but this statistic is not reliable. Specification error in the latent Markov model focuses on the transition probabilities when a longitudinal Guttman process is incorrectly specified. The findings show that specifying a longitudinal Guttman process that is not true in the population impacts other transition probabilities through the covariance matrix of the logit parameters used to calculate those probabilities. 相似文献
7.
The analysis of interaction among latent variables has received much attention. This article introduces a Bayesian approach to analyze a general structural equation model that accommodates the general nonlinear terms of latent variables and covariates. This approach produces a Bayesian estimate that has the same statistical optimal properties as a maximum likelihood estimate. Other advantages over the traditional approaches are discussed. More important, we demonstrate through examples how to use the freely available software WinBUGS to obtain Bayesian results for estimation and model comparison. Simulation studies are conducted to assess the empirical performances of the approach for situations with various sample sizes and prior inputs. 相似文献
8.
Michael C. Neale Shaunna L. Clark Conor V. Dolan Michael D. Hunter 《Structural equation modeling》2016,23(2):221-233
A linear latent growth curve mixture model with regime switching is extended in 2 ways. Previously, the matrix of first-order Markov switching probabilities was specified to be time-invariant, regardless of the pair of occasions being considered. The first extension, time-varying transitions, specifies different Markov transition matrices between each pair of occasions. The second extension is second-order time-invariant Markov transition probabilities, such that the probability of switching depends on the states at the 2 previous occasions. The models are implemented using the R package OpenMx, which facilitates data handling, parallel computation, and further model development. It also enables the extraction and display of relative likelihoods for every individual in the sample. The models are illustrated with previously published data on alcohol use observed on 4 occasions as part of the National Longitudinal Survey of Youth, and demonstrate improved fit to the data. 相似文献
9.
Structural equation modeling is a common multivariate technique for the assessment of the interrelationships among latent variables. Structural equation models have been extensively applied to behavioral, medical, and social sciences. Basic structural equation models consist of a measurement equation for characterizing latent variables through multiple observed variables and a mean regression-type structural equation for investigating how explanatory latent variables influence outcomes of interest. However, the conventional structural equation does not provide a comprehensive analysis of the relationship between latent variables. In this article, we introduce the quantile regression method into structural equation models to assess the conditional quantile of the outcome latent variable given the explanatory latent variables and covariates. The estimation is conducted in a Bayesian framework with Markov Chain Monte Carlo algorithm. The posterior inference is performed with the help of asymmetric Laplace distribution. A simulation shows that the proposed method performs satisfactorily. An application to a study of chronic kidney disease is presented. 相似文献
10.
In this article, we propose a nonlinear dynamic latent class structural equation modeling (NDLC-SEM). It can be used to examine intra-individual processes of observed or latent variables. These processes are decomposed into parts which include individual- and time-specific components. Unobserved heterogeneity of the intra-individual processes are modeled via a latent Markov process that can be predicted by individual- and time-specific variables as random effects. We discuss examples of sub-models which are special cases of the more general NDLC-SEM framework. Furthermore, we provide empirical examples and illustrate how to estimate this model in a Bayesian framework. Finally, we discuss essential properties of the proposed framework, give recommendations for applications, and highlight some general problems in the estimation of parameters in comprehensive frameworks for intensive longitudinal data. 相似文献
11.
Despite the widespread popularity of growth curve analysis, few studies have investigated robust growth curve models. In this article, the t distribution is applied to model heavy-tailed data and contaminated normal data with outliers for growth curve analysis. The derived robust growth curve models are estimated through Bayesian methods utilizing data augmentation and Gibbs sampling algorithms. The analysis of mathematical development data shows that the robust latent basis growth curve model better describes the mathematical growth trajectory than the corresponding normal growth curve model and can reveal the individual differences in mathematical development. Simulation studies further confirm that the robust growth curve models significantly outperform the normal growth curve models for both heavy-tailed t data and normal data with outliers but lose only slight efficiency for normal data. It appears convincing to replace the normal distribution with the t distribution for growth curve analysis. Three information criteria are evaluated for model selection. Online software is also provided for conducting robust analysis discussed in this study. 相似文献
12.
13.
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
14.
Kilchan Choi 《Asia Pacific Education Review》2001,2(1):44-55
This paper presents a strategy for specifying latent variable regressions in the hierarchical modeling framework (LVR-HM).
This model takes advantage of the Structural Equation Modeling (SEM) approach in terms of modeling flexibility—regression
among latent variables—and of the HM approach in terms of allowing for more general data structures. A fully Bayesian approach
via Markov Chain Monte Carlo (MCMC) techniques is applied to the LVR-HM. Through analyzing the data from a longitudinal study
of educational achievement, gender difference are explored in the growth of mathematical achievement across grade 7 through
grade 10. Allowing for the fact that initial status effect to rates of change may differ for girls and boys, the LVR-HM is
specified in a way that rates of change parameters are modeled as a function of initial status parameters and the interaction
between initial status and gender. 相似文献
15.
During the early phases of research, semiparametric models (SPMs) have the advantage of recovering latent nonlinearity over parametric counterparts. Structural equation mixture models (Bauer, 2005) can be applied as SPMs to flexibly recover and describe the form of the unknown latent relationship with minimal distributional assumptions. This short report extends the work on this SPM (Bauer, 2005; Pek, Losardo & Bauer, 2011) by developing approximate simultaneous confidence bands or confidence envelopes (CEs) to evaluate potential nonlinearity of the unknown latent function. A line-finding algorithm to be used in conjunction with these CEs is also developed as an implementation of an informal test to diagnose nonlinearity. Coverage of the CEs and performance of the algorithm in terms of rates of detecting latent nonlinearity are evaluated by Monte Carlo. Recommendations for the use of these CEs and the algorithm for detecting nonlinearity are suggested. 相似文献
16.
Hefei Liu 《Structural equation modeling》2018,25(1):41-55
Multivariate heterogenous data with latent variables are common in many fields such as biological, medical, behavioral, and social-psychological sciences. Mixture structural equation models are multivariate techniques used to examine heterogeneous interrelationships among latent variables. In the analysis of mixture models, determination of the number of mixture components is always an important and challenging issue. This article aims to develop a full Bayesian approach with the use of reversible jump Markov chain Monte Carlo method to analyze mixture structural equation models with an unknown number of components. The proposed procedure can simultaneously and efficiently select the number of mixture components and conduct parameter estimation. Simulation studies show the satisfactory empirical performance of the method. The proposed method is applied to study risk factors of osteoporotic fractures in older people. 相似文献
17.
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. 相似文献
18.
Multilevel Structural equation models are most often estimated from a frequentist framework via maximum likelihood. However, as shown in this article, frequentist results are not always accurate. Alternatively, one can apply a Bayesian approach using Markov chain Monte Carlo estimation methods. This simulation study compared estimation quality using Bayesian and frequentist approaches in the context of a multilevel latent covariate model. Continuous and dichotomous variables were examined because it is not yet known how different types of outcomes—most notably categorical—affect parameter recovery in this modeling context. Within the Bayesian estimation framework, the impact of diffuse, weakly informative, and informative prior distributions were compared. Findings indicated that Bayesian estimation may be used to overcome convergence problems and improve parameter estimate bias. Results highlight the differences in estimation quality between dichotomous and continuous variable models and the importance of prior distribution choice for cluster-level random effects. 相似文献
19.
Semicontinuous variable analysis is a widely appreciated statistical method in such disciplines as social science, medicines, and economics. In detecting underlying structure and representing possible interrelationships, statistical analysis using a two-part model is appropriated. In this paper, we present a general extension of two-part model to the situation where the unobserved factors are included in the two parts to interpret external variability in semicontinuous variable. Auxiliary information on these factors is manifested by continuous responses via measurement model, while the interrelationships among factors are exploited through structural equation model. Moreover, under longitudinal setting, dynamic characteristics of responses between any two occasions are represented by transition model. Procedures for model fitting, parameter estimation, model selection and prediction are developed within the Bayesian paradigm. Markov Chains Monte Carlo method is used to implement posterior analysis. Empirical results including a simulation and a real example are used to illustrate the proposed methodology. 相似文献
20.
The primary goal of this article is to demonstrate the close relationship between 2 classes of dynamic models in psychological research: latent change score models and continuous time models. The secondary goal is to point out some differences. We begin with a brief review of both approaches, before demonstrating how the 2 methods are mathematically and conceptually related. It will be shown that most commonly used latent change score models are related to continuous time models by the difference equation approximation to the differential equation. One way in which the 2 approaches differ is the treatment of time. Whereas there are theoretical and practical restrictions regarding observation time points and intervals in latent change score models, no such limitations exist in continuous time models. We illustrate our arguments with three simulated data sets using a univariate and bivariate model with equal and unequal time intervals. As a by-product of this comparison, we discuss the use of phantom and definition variables to account for varying time intervals in latent change score models. We end with a reanalysis of the Bradway–McArdle longitudinal study on intellectual abilities (used before by McArdle & Hamagami, 2004) by means of the proportional change score model and the dual change score model in discrete and continuous time. 相似文献