Variable selection via the weighted group lasso for factor analysis models

DOI10.1002/cjs.11129zbMath1349.62250OpenAlexW2091771402MaRDI QIDQ2856545

Kei Hirose, Sadanori Konishi

Publication date: 29 October 2013

Published in: Canadian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/cjs.11129

zbMATH Keywords

factor analysisvariable selection\(L_1\) regularizationnumber of factorsweighted group Lasso

Mathematics Subject Classification ID

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

Related Items (10)

A deep learning algorithm for high-dimensional exploratory item factor analysis ⋮ Globally sparse and locally dense signal recovery for compressed sensing ⋮ Assuming independence in spatial latent variable models: Consequences and implications of misspecification ⋮ Structural factor equation models for causal network construction via directed acyclic mixed graphs ⋮ Shrinkage estimation of dynamic panel data models with interactive fixed effects ⋮ Simultaneous variable and factor selection via sparse group lasso in factor analysis ⋮ Principal component selection via adaptive regularization method and generalized information criterion ⋮ Approximated penalized maximum likelihood for exploratory factor analysis: an orthogonal case ⋮ Parsimonious Bayesian factor analysis for modelling latent structures in spectroscopy data ⋮ Sparse estimation via nonconcave penalized likelihood in factor analysis model

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• The Adaptive Lasso and Its Oracle Properties
• The varimax criterion for analytic rotation in factor analysis
• Factor analysis and AIC
• EM algorithms for ML factor analysis
• Estimating the dimension of a model
• On various causes of improper solutions in maximum likelihood factor analysis
• Least angle regression. (With discussion)
• Identification of inconsistent variates in factor analysis
• Stepwise variable selection in factor analysis
• Nonlinear regression modeling via the lasso-type regularization
• Information criteria and statistical modeling.
• On the ``degrees of freedom of the lasso
• Some contributions to maximum likelihood factor analysis
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Regularization and Variable Selection Via the Elastic Net
• Model Selection and Estimation in Regression with Grouped Variables
• Bayesian information criteria and smoothing parameter selection in radial basis function networks
• A RAPIDLY CONVERGENT METHOD FOR MAXIMUM‐LIKELIHOOD FACTOR ANALYSIS

This page was built for publication: Variable selection via the weighted group lasso for factor analysis models