4 papers · 1 filter
Finite-Sample Distribution Theory and Efficient Large-Scale Inference for Online Quantile Regression
Ziyang Wei, Jiaqi Li, Lan Wang +1
This paper studies online quantile regression for large-scale and streaming data using Stochastic SubGradient Descent (SSGD) with constant learning rates. Classical offline inferen…
Refining Covariance Matrix Estimation in Stochastic Gradient Descent Through Bias Reduction
Ziyang Wei, Wanrong Zhu, Jingyang Lyu +1
We study online inference and asymptotic covariance estimation for the stochastic gradient descent (SGD) algorithm. While classical methods (such as plug-in and batch-means estimat…
Central Limit Theorems for Stochastic Gradient Descent Quantile Estimators
Ziyang Wei, Jiaqi Li, Likai Chen +1
This paper develops asymptotic theory for quantile estimation via stochastic gradient descent (SGD) with a constant learning rate. The quantile loss function is neither smooth nor…
High Confidence Level Inference is Almost Free using Parallel Stochastic Optimization
Wanrong Zhu, Zhipeng Lou, Ziyang Wei +1
Uncertainty quantification for estimation through stochastic optimization solutions in an online setting has gained popularity recently. This paper introduces a novel inference met…