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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.
Getting started
README.md
Introduction to `ssp.glm`: Subsampling for Generalized Linear Models
Introduction to `ssp.quantreg`: Subsampling for Quantile Regression
Introduction to `ssp.relogit`: Subsampling for Logistic Regression Model with Rare Events
Introduction to `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 | 0.1.1 |
| URL | https://github.com/dqksnow/Subsampling |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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