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 |
| DOI: | 10.32614/CRAN.package.SGDinference |
| 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 |
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