Pages that link to "Item:Q2364845"
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The following pages link to Latent variable selection for multidimensional item response theory models via \(L_{1}\) regularization (Q2364845):
Displaying 12 items.
- On latent trait estimation in multidimensional compensatory item response models (Q748211) (← links)
- A deep learning algorithm for high-dimensional exploratory item factor analysis (Q823855) (← links)
- A likelihood-based boosting algorithm for factor analysis models with binary data (Q2076167) (← links)
- Computation for latent variable model estimation: a unified stochastic proximal framework (Q2103576) (← links)
- Multidimensional item response theory in the style of collaborative filtering (Q2141657) (← links)
- A partially confirmatory approach to the multidimensional item response theory with the Bayesian Lasso (Q2220543) (← links)
- Robust measurement via a fused latent and graphical item response theory model (Q2318816) (← links)
- Joint maximum likelihood estimation for high-dimensional exploratory item factor analysis (Q2331148) (← links)
- Penalized Item Response Theory Models: Application to Epigenetic Alterations in Bladder Cancer (Q5449936) (← links)
- Latent variable selection in multidimensional item response theory models using the expectation model selection algorithm (Q6126890) (← links)
- A generalized expectation model selection algorithm for latent variable selection in multidimensional item response theory models (Q6190672) (← links)
- Regularized variational estimation for exploratory item factor analysis (Q6572344) (← links)
