Author

Ye Shi

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Preprint Sep 2026

Optimizer as Detector: Stochastic Gradient Descent for Latent Mixture Models

A detector based on the steady-state dynamics of constant-step stochastic gradient descent (SGD) is proposed, which extends the method to intercept and partial-mixture heterogeneity and study endogeneity, heteroskedasticity, and nonlinear misspecification.

Ye Shi, Xiao Jin, Chung-Piaw Teo · 0 citations