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Activity Number:
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27
- SPEED: Statistical Learning and Data Challenge Part 1
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Type:
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Contributed
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Date/Time:
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Sunday, August 7, 2022 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #323045
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Title:
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Stochastic Gradient Descent for Estimation and Inference in Spatial Quantile Models
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Author(s):
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Gan Luan and Jimeng Loh*
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Companies:
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New Jersey Institute of Technology and NJIT
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Keywords:
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Stochastic gradient descent;
spatial autoregressive model;
quantile regression
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Abstract:
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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.
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Authors who are presenting talks have a * after their name.
