SGDinference: Inference with Stochastic Gradient Descent
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.
The methodology used in the 'SGDinference' package is described in detail in the following papers:
(i) Lee, S., Liao, Y., Seo, M.H. and Shin, Y. (2022) <doi:10.1609/aaai.v36i7.20701> "Fast and robust online inference with stochastic gradient descent via random scaling".
(ii) Lee, S., Liao, Y., Seo, M.H. and Shin, Y. (2023) <doi:10.48550/arXiv.2209.14502> "Fast Inference for Quantile Regression with Tens of Millions of Observations".
| Version: |
0.1.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
stats, Rcpp (≥ 1.0.5) |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
knitr, rmarkdown, testthat (≥ 3.0.0), lmtest (≥ 0.9), sandwich (≥ 3.0), microbenchmark (≥ 1.4), conquer (≥ 1.3.3) |
| Published: |
2023-11-16 |
| Author: |
Sokbae Lee [aut],
Yuan Liao [aut],
Myung Hwan Seo [aut],
Youngki Shin [aut, cre] |
| Maintainer: |
Youngki Shin <shiny11 at mcmaster.ca> |
| BugReports: |
https://github.com/SGDinference-Lab/SGDinference/issues |
| License: |
GPL-3 |
| URL: |
https://github.com/SGDinference-Lab/SGDinference/ |
| NeedsCompilation: |
yes |
| Materials: |
README NEWS |
| CRAN checks: |
SGDinference results |
Documentation:
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