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.
Generalized Linear Models (GLMs)
Softmax (Multinomial) Regression
Rare Event Logistic Regression
Quantile Regression
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