Latent variable selection in structural equation models

DOI10.1016/j.jmva.2016.08.004zbMath1349.62216OpenAlexW2514548461WikidataQ57535651 ScholiaQ57535651MaRDI QIDQ321933

Yan-Qing Zhang, Guo-Liang Tian, Nian Sheng Tang

Publication date: 14 October 2016

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2016.08.004

zbMATH Keywords

variable selectionLassostructural equation modelsECM algorithmSCAD

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07)

Related Items (1)

An approach for knowledge acquisition from a survey data by conducting Bayesian network modeling, adopting the robust coplot method

Uses Software

• softImpute

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• The Adaptive Lasso and Its Oracle Properties
• Maximum likelihood estimation of nonlinear structural equation models
• Analysis of structural equation model with ignorable missing continuous and polytomous data
• One-step sparse estimates in nonconcave penalized likelihood models
• Nonconcave penalized likelihood with a diverging number of parameters.
• Model comparison of nonlinear structural equation models with fixed covariates
• A Bayesian modeling approach for generalized semiparametric structural equation models
• Variable selection using MM algorithms
• Joint Variable Selection for Fixed and Random Effects in Linear Mixed-Effects Models
• Maximum likelihood estimation via the ECM algorithm: A general framework
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• A Note on the Efficiency of Sandwich Covariance Matrix Estimation
• A Statistical View of Some Chemometrics Regression Tools
• Basic and Advanced Bayesian Structural Equation Modeling
• Regularization and Variable Selection Via the Elastic Net
• Model Selection and Estimation in Regression with Grouped Variables
• Tuning parameter selectors for the smoothly clipped absolute deviation method

This page was built for publication: Latent variable selection in structural equation models