SAUSS: Stochastic Approximation with Unbiased Simulated Scores for Limited Dependent Variable Models
Multinomial choice models allow flexible substitution patterns but become computationally demanding with many alternatives or observations. With a fixed per-observation simulation budget, simulated maximum likelihood introduces simulation bias, while each optimization step requires a full-sample likelihood evaluation. We propose Stochastic Approximation with Unbiased Simulated Scores (SAUSS), an averaged stochastic approximation based on conditionally unbiased mini-batch score estimates. Each iteration uses a fixed mini-batch regardless of sample size. For multinomial probit, accept-reject sampling provides exact conditional draws and unbiased score estimates for any fixed number of accep...
| Authors | Sokbae Lee, Yuan Liao, Myung Hwan Seo et al. |
|---|---|
| Primary category | stat.ME |
| Published | 26 August 2026 |
| Last updated at source | 26 August 2026 |
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