SAUSS: Stochastic Approximation with Unbiased Simulated Scores for Limited Dependent Variable Models

arXiv:2608.25304 · stat.ME, cs.LG, econ.EM · Submitted 2026-08-26 · Read on arXiv

stat.ME, cs.LG, econ.EM

Submitted: 2026-08-26

Updated: 2026-08-26

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

Terminology

• Consistency — 8× in this paper · explained in 3 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Benchmark — 4× in this paper · explained in 2 episodes
• Data Augmentation — 3× in this paper · explained in 7 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Stochastic Approximation with Unbiased Simulated Scores — 2× in this paper
• Computational Tractability — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes

Sources

• SLIM: Stochastic Learning and Inference in Nonlinear GMM Models
• Fast Online Inference on Semiparametric Models
• Computationally Efficient Estimation of Large Probit Models
• Amortized Inference for Correlated Discrete Choice Models via Equivariant Neural Networks

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