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subsampling: Optimal Subsampling Methods for Statistical Models
Balancing computational and statistical efficiency, subsampling techniques offer a practical solution for handling large-scale data analysis. Subsampling methods enhance statistical modeling for massive datasets by efficiently drawing representative subsamples from full dataset based on tailored sampling probabilities. These probabilities are optimized for specific goals, such as minimizing the variance of coefficient estimates or reducing prediction error. Based on specified modeling assumptions and subsampling techniques, the package provides functions to draw subsamples from the full data, fit the model on the subsamples, and perform statistical inference.
Getting started
README.md
`ssp.glm.rF`: Balanced Subsampling for Preserving Rare Features in Generalized Linear Models
`ssp.glm`: Subsampling for Generalized Linear Models
`ssp.quantreg`: Subsampling for Quantile Regression
`ssp.relogit`: Subsampling for Logistic Regression Model with Rare Events
`ssp.softmax`: Subsampling for Softmax (Multinomial) Regression Model
Browse package contents
Package details |
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| Author | Qingkai Dong [aut, cre, cph], Yaqiong Yao [aut], Haiying Wang [aut], Qiang Zhang [ctb], Jun Yan [ctb] |
| Maintainer | Qingkai Dong <qingkai.dong@uconn.edu> |
| License | GPL-3 |
| Version | 0.3.0 |
| URL | https://github.com/dqksnow/subsampling https://dqksnow.github.io/subsampling/ |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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