An efficient adaptive dimension selection algorithm for multidimensional probit graded response models

arXiv:2607.17654 · stat.ML, cs.LG, stat.ME · Submitted 2026-07-20 · Read on arXiv

Yu Zhou, Yincai Tang, Bin Lv, Meng Gao

stat.ML, cs.LG, stat.ME

Submitted: 2026-07-20

Comments: 36 pages, 3 figures, 5 tables

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

Accuracy — 13× in this paper · explained in 1 episode
Augmentation — 11× in this paper · explained in 2 episodes
Benchmark — 6× in this paper · explained in 2 episodes
Data Augmentation — 6× in this paper · explained in 10 episodes
Sparsity — 5× in this paper · explained in 5 episodes
Computational Efficiency — 4× in this paper · explained in 2 episodes
Interpretability — 4× in this paper · explained in 15 episodes
Latent Space — 4× in this paper · explained in 7 episodes
Bayesian Framework — 2× in this paper · explained in 3 episodes
Monotonicity — 2× in this paper · explained in 2 episodes
Overfitting — 2× in this paper · explained in 2 episodes
Consistency — 1× in this paper · explained in 3 episodes
Degeneracy — 1× in this paper · explained in 4 episodes
Hierarchical Structure — 1× in this paper · explained in 3 episodes
Hyperparameters — 1× in this paper · explained in 2 episodes
Lasso — 1× in this paper · explained in 2 episodes
Regularization — 1× in this paper · explained in 3 episodes
Robustness — 1× in this paper · explained in 6 episodes
Systematic Bias — 1× in this paper · explained in 3 episodes

Sources

Sparse Bayesian Factor Analysis when the Number of Factors is Unknown

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