SGDinference: SGDinference
In SGDinference: Inference with Stochastic Gradient Descent
SGDinference R Documentation
SGDinference
Description
The 'SGDinference' package provides estimation and inference methods for large-scale mean and quantile regression models via stochastic (sub-)gradient descent (S-subGD) algorithms.
The inference procedure handles cross-sectional data sequentially:
(i) updating the parameter estimate with each incoming "new observation",
(ii) aggregating it as a Polyak-Ruppert average, and
(iii) computing an asymptotically pivotal statistic for inference through random scaling.
Author(s)
Sokbae Lee, Yuan Liao, Myung Hwan Seo, Youngki Shin
SGDinference documentation built on Nov. 17, 2023, 1:12 a.m.
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| SGDinference | R Documentation |
SGDinference
Description
The 'SGDinference' package provides estimation and inference methods for large-scale mean and quantile regression models via stochastic (sub-)gradient descent (S-subGD) algorithms. The inference procedure handles cross-sectional data sequentially: (i) updating the parameter estimate with each incoming "new observation", (ii) aggregating it as a Polyak-Ruppert average, and (iii) computing an asymptotically pivotal statistic for inference through random scaling.
Author(s)
Sokbae Lee, Yuan Liao, Myung Hwan Seo, Youngki Shin
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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