A penalized likelihood method for structural equation modeling

DOI10.1007/s11336-017-9566-9zbMath1402.62126OpenAlexW2607263590WikidataQ38833354 ScholiaQ38833354MaRDI QIDQ1695631

Po-Hsien Huang, Hung Chen, Li-Jen Weng

Publication date: 7 February 2018

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11336-017-9566-9

zbMATH Keywords

model selectionoracle propertyECM algorithmpenalized likelihoodstructural equation modelingfactor analysis model

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05) Generalized linear models (logistic models) (62J12) Applications of statistics to psychology (62P15)

Related Items (4)

Single- and multiple-group penalized factor analysis: a trust-region algorithm approach with integrated automatic multiple tuning parameter selection ⋮ Latent variable selection in multidimensional item response theory models using the expectation model selection algorithm ⋮ Sparse and simple structure estimation via prenet penalization ⋮ Path and directionality discovery in individual dynamic models: a regularized unified structural equation modeling approach for hybrid vector autoregression

Uses Software

• R
• lavaan
• DIFlasso
• sparsenet
• lsl

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection
• Nearly unbiased variable selection under minimax concave penalty
• The Adaptive Lasso and Its Oracle Properties
• Sparse estimation via nonconcave penalized likelihood in factor analysis model
• A penalized maximum likelihood approach to sparse factor analysis
• Maximum likelihood estimation of nonlinear structural equation models
• Statistics for high-dimensional data. Methods, theory and applications.
• On the investigation of local identifiability: A counterexample
• Can one estimate the conditional distribution of post-model-selection estimators?
• Sparse versus simple structure loadings
• Variable selection for generalized linear mixed models by \(L_1\)-penalized estimation
• Discussion: One-step sparse estimates in nonconcave penalized likelihood models: Who cares if it is a white cat or a black cat?
• Estimating the dimension of a model
• Heuristics of instability and stabilization in model selection
• Estimation of an oblique structure via penalized likelihood factor analysis
• Nonconcave penalized likelihood with a diverging number of parameters.
• Variable selection in linear mixed effects models
• Hierarchical Bayes, maximum a posteriori estimators, and minimax concave penalized likelihood estimation
• A penalty approach to differential item functioning in Rasch models
• Pathwise coordinate optimization
• Large sample properties of the smoothly clipped absolute deviation penalized maximum likelihood estimation on high dimensions
• Estimation for High-Dimensional Linear Mixed-Effects Models Using ℓ1-Penalization
• Fixed and Random Effects Selection in Mixed Effects Models
• SparseNet: Coordinate Descent With Nonconvex Penalties
• Maximum likelihood estimation via the ECM algorithm: A general framework
• Estimation of a covariance matrix with zeros
• Ideal spatial adaptation by wavelet shrinkage
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Development and validation of brief measures of positive and negative affect: The PANAS scales.
• Asymptotically distribution‐free methods for the analysis of covariance structures
• Regularization Parameter Selections via Generalized Information Criterion
• Nonconcave Penalized Likelihood With NP-Dimensionality
• Statistical Analysis ofQ-Matrix Based Diagnostic Classification Models
• OBLIQUE ROTATION TO A PARTIALLY SPECIFIED TARGET
• A new look at the statistical model identification

This page was built for publication: A penalized likelihood method for structural equation modeling