A likelihood-based boosting algorithm for factor analysis models with binary data
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Publication:2076167
DOI10.1016/j.csda.2021.107412OpenAlexW4200215438MaRDI QIDQ2076167
Publication date: 18 February 2022
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2021.107412
Uses Software
Cites Work
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- Greedy function approximation: A gradient boosting machine.
- On the choice and influence of the number of boosting steps for high-dimensional linear Cox-models
- On composite marginal likelihoods
- Boosting in Cox regression: a comparison between the likelihood-based and the model-based approaches with focus on the R-packages \textit{CoxBoost} and \textit{mboost}
- Multidimensional item reponse theory.
- High-dimensional exploratory item factor analysis by a Metropolis-Hastings Robbins-Monro algorithm
- RcppArmadillo: accelerating R with high-performance C++ linear algebra
- On the numerical approximation of the bivariate normal (tetrachoric) correlation coefficient
- Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
- Pairwise likelihood estimation for factor analysis models with ordinal data
- High-dimensional maximum marginal likelihood item factor analysis by adaptive quadrature
- Rating scales as predictors -- the old question of scale level and some answers
- Latent variable selection for multidimensional item response theory models via \(L_{1}\) regularization
- Nonconvex optimization using negative curvature within a modified linesearch
- MCMC estimation and some model-fit analysis of multidimensional IRT models
- Pairwise likelihood approach to grouped continuous model and its extension
- Latent Variable Models and Factor Analysis
- A note on composite likelihood inference and model selection
- A modification of Armijo's step-size rule for negative curvature
- Exploiting negative curvature directions in linesearch methods for unconstrained optimization
- Generalized Additive Modeling with Implicit Variable Selection by Likelihood‐Based Boosting
- Model Selection and Estimation in Regression with Grouped Variables
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