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Structural Equation Modeling: A Multidisciplinary Journal Volume 25, 2018 - Issue 6
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Original Articles

An Adaptive Bayesian Lasso Approach with Spike-and-Slab Priors to Identify Multiple Linear and Nonlinear Effects in Structural Equation Models

Holger BrandtUniversity of KansasCorrespondence[email protected]
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,
Jenna CambriaUniversity of ArkansasView further author information
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Augustin KelavaEberhard Karls University TuebingenView further author information
Pages 946-960 | Published online: 12 Jun 2018

Citations (12)

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Read on this site (8)

Zachary J. Roman & Holger Brandt. (2023) A Latent Auto-Regressive Approach for Bayesian Structural Equation Modeling of Spatially or Socially Dependent Data. Multivariate Behavioral Research 58:1, pages 90-114.
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Siyuan Marco Chen, Daniel J. Bauer, William M. Belzak & Holger Brandt. (2022) Advantages of Spike and Slab Priors for Detecting Differential Item Functioning Relative to Other Bayesian Regularizing Priors and Frequentist Lasso. Structural Equation Modeling: A Multidisciplinary Journal 29:1, pages 122-139.
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Lijin Zhang, Junhao Pan & Edward Haksing Ip. (2021) Criteria for Parameter Identification in Bayesian Lasso Methods for Covariance Analysis: Comparing Rules for Thresholding, p-value, and Credible Interval. Structural Equation Modeling: A Multidisciplinary Journal 28:6, pages 941-950.
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W. Holmes Finch & J. E. Miller. (2021) A Comparison of Regularized Maximum-Likelihood, Regularized 2-Stage Least Squares, and Maximum-Likelihood Estimation with Misspecified Models, Small Samples, and Weak Factor Structure. Multivariate Behavioral Research 56:4, pages 608-626.
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Xinya Liang & Ross Jacobucci. (2020) Regularized Structural Equation Modeling to Detect Measurement Bias: Evaluation of Lasso, Adaptive Lasso, and Elastic Net. Structural Equation Modeling: A Multidisciplinary Journal 27:5, pages 722-734.
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Axel Mayer, Johannes Zimmermann, Jürgen Hoyer, Simone Salzer, Jörg Wiltink, Eric Leibing & Falk Leichsenring. (2020) Interindividual Differences in Treatment Effects Based on Structural Equation Models with Latent Variables: An EffectLiteR Tutorial. Structural Equation Modeling: A Multidisciplinary Journal 27:5, pages 798-816.
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Daniel J. Bauer, William C. M. Belzak & Veronica T. Cole. (2020) Simplifying the Assessment of Measurement Invariance over Multiple Background Variables: Using Regularized Moderated Nonlinear Factor Analysis to Detect Differential Item Functioning. Structural Equation Modeling: A Multidisciplinary Journal 27:1, pages 43-55.
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Augustin Kelava & Holger Brandt. (2019) A Nonlinear Dynamic Latent Class Structural Equation Model. Structural Equation Modeling: A Multidisciplinary Journal 26:4, pages 509-528.
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Articles from other publishers (4)

Sara van Erp. (2023) Bayesian Regularized SEM: Current Capabilities and Constraints. Psych 5:3, pages 814-835.
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Zhonglin WEN, Jinying OUYANG & Junyan FANG. (2022) Standardized estimates for latent interaction effects: Method comparison and selection strategy. Acta Psychologica Sinica 54:1, pages 91-107.
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William C.M. Belzak & Daniel J. Bauer. (2019) Interaction effects may actually be nonlinear effects in disguise: A review of the problem and potential solutions. Addictive Behaviors 94, pages 99-108.
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Ross Jacobucci, Andreas M. Brandmaier & Rogier A. Kievit. (2019) A Practical Guide to Variable Selection in Structural Equation Modeling by Using Regularized Multiple-Indicators, Multiple-Causes Models. Advances in Methods and Practices in Psychological Science 2:1, pages 55-76.
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