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Activity Number: 72 - SPEED: Statistical Learning and Data Challenge Part 2
Type: Contributed
Date/Time: Sunday, August 7, 2022 : 4:00 PM to 4:45 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #323714
Title: Stochastic Gradient Descent for Estimation and Inference in Spatial Quantile Models
Author(s): Gan Luan and Jimeng Loh*
Companies: New Jersey Institute of Technology and NJIT
Keywords: Stochastic gradient descent; spatial autoregressive model; quantile regression

We consider using stochastic gradient descent (SGD) procedure to fit spatial auto-regressive quantile models to lattice data, incorporating a recently developed perturbation method to obtain standard errors in addition to model parameter estimates. We derive the SGD update equations and perform a simulation study to examine the empirical coverage of confidence intervals constructed using the perturbation procedure.

Authors who are presenting talks have a * after their name.

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