Pages that link to "Item:Q2856545"

The following pages link to Variable selection via the weighted group Lasso for factor analysis models (Q2856545):

Displaying 13 items.

• Sparse estimation via nonconcave penalized likelihood in factor analysis model (Q261015) (← links)
• Principal component selection via adaptive regularization method and generalized information criterion (Q513693) (← links)
• A deep learning algorithm for high-dimensional exploratory item factor analysis (Q823855) (← links)
• Shrinkage estimation of dynamic panel data models with interactive fixed effects (Q894645) (← links)
• Parsimonious Bayesian factor analysis for modelling latent structures in spectroscopy data (Q2080755) (← links)
• Approximated penalized maximum likelihood for exploratory factor analysis: an orthogonal case (Q2318820) (← links)
• Globally sparse and locally dense signal recovery for compressed sensing (Q2410784) (← links)
• Grouping Variable Selection by Weight Fused Elastic Net for Multi-Collinear Data (Q2905731) (← links)
• Simultaneous variable and factor selection via sparse group lasso in factor analysis (Q5107489) (← links)
• Group variable selection via SCAD-<i>L</i>₂ (Q5169751) (← links)
• Assuming independence in spatial latent variable models: Consequences and implications of misspecification (Q6055531) (← links)
• Structural factor equation models for causal network construction via directed acyclic mixed graphs (Q6074501) (← links)
• Mediation analysis with latent factors using simultaneous group-wise and parameter-wise penalization (Q6548890) (← links)