subsampling: Optimal Subsampling Methods for Statistical Models

In subsampling: Optimal Subsampling Methods for Statistical Models

Optimal Subsampling Methods for Statistical Models

Description

Subsampling methods are utilized in statistical modeling for massive datasets. These methods aim to draw representative subsamples from the full dataset based on specific sampling probabilities, with the goal of maintaining inference efficiency. The sampling probabilities are tailored to particular objectives, such as minimizing the variance of the estimated coefficients or reducing prediction error. By using subsampling techniques, the package balances the trade-off between computational efficiency and statistical efficiency, making it a practical tool for massive data analysis.

Models Supported

• Generalized Linear Models (GLMs)

• Softmax (Multinomial) Regression

• Rare Event Logistic Regression

• Quantile Regression

Author(s)

Maintainer: Qingkai Dong [e-mail] [copyright holder]

Authors:

• Yaqiong Yao

• Haiying Wang

Other contributors:

• Qiang Zhang [contributor]

• Jun Yan [contributor]

See Also

Useful links:

• https://github.com/dqksnow/Subsampling

• Report bugs at https://github.com/dqksnow/Subsampling/issues

subsampling documentation built on April 12, 2025, 1:50 a.m.