Variational Approach for Unconditional Latent Curve Models

Tiwari, Puneet

doi:10.2139/ssrn.2863822

Cited by 2 publications

(1 citation statement)

References 34 publications

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“…However, MCMC methods may suffer from slow convergence and long running times, compared to frequentist approaches. Variational approximations can be a fast alternative; however they have not been adequately explored for this class of models, with current work limited to a variational method for a special case of latent curve models (Tiwari, 2016). In this paper, we propose and study a mean field variational Bayes (MFVB) approach to fit a general class of SEMs.…”

Section: Introductionmentioning

confidence: 99%

Fitting Structural Equation Models via Variational Approximations

Dang¹,

Maestrini²

2021

Preprint

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Structural equation models are commonly used to capture the structural relationship between sets of observed and unobservable variables. In Bayesian settings, fitting and inference for these models are typically performed via Markov chain Monte Carlo methods that may be computationally intensive, especially for models with a large number of manifest variables or complex structures. Variational approximations can be a fast alternative; however they have not been adequately explored for this class of models. We develop a mean field variational Bayes approach for fitting basic structural equation models. We show that this variational approximation method can provide reliable inference while being significantly faster than Markov chain Monte Carlo. Classical mean field variational Bayes may underestimate the true posterior variance, therefore we propose and study bootstrap to overcome this issue. We discuss different inference strategies based on bootstrap and demonstrate how these can considerably improve the accuracy of the variational approximation through real and simulated examples.

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Section: Introductionmentioning

confidence: 99%

Fitting Structural Equation Models via Variational Approximations

Dang¹,

Maestrini²

2021

Preprint

View full text Add to dashboard Cite

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Fitting Structural Equation Models via Variational Approximations

Dang

Maestrini

2022

Structural Equation Modeling: A Multidisciplinary Journal

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Structural equation models (SEMs) are commonly used to study the structural relationship between observed variables and latent constructs. Recently, Bayesian fitting procedures for SEMs have received more attention thanks to their potential to facilitate the adoption of more flexible model structures, and variational approximations have been shown to provide fast and accurate inference for Bayesian analysis of SEMs. However, the application of variational approximations is currently limited to very simple, elemental SEMs. We develop mean-field variational Bayes algorithms for two SEM formulations for data that present non-Gaussian features such as skewness and multimodality. The proposed models exploit the use of mixtures of Gaussians, include covariates for the analysis of latent traits and consider missing data. We also examine two variational information criteria for model selection that are straightforward to compute in our variational inference framework. The performance of the MFVB algorithms and information criteria is investigated in a simulated data study and a real data application.

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Variational Approach for Unconditional Latent Curve Models

Cited by 2 publications

References 34 publications

Fitting Structural Equation Models via Variational Approximations

Fitting Structural Equation Models via Variational Approximations

Fitting Structural Equation Models via Variational Approximations

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