plot_AMSE

After using the subsampling methods under potential model misspecification we obtain
their respective AMSE values for the predictions. They are summarised as plots here.

Subsampling methods for big data under different models and assumptions.
Starting with linear regression and leading to Generalised Linear Models, softmax
regression, and quantile regression. Specifically, the model-robust subsampling method
proposed in Mahendran, A., Thompson, H., and McGree, J. M. (2023) <doi:10.1007/s00362-023-01446-9>,
where multiple models can describe the big data, and the subsampling framework for potentially
misspecified Generalised Linear Models in Mahendran, A., Thompson, H., and McGree, J. M. (2025)
<doi:10.48550/arXiv.2510.05902>.

Amalan Mahendran

NeEDS4BigData

New Experimental Design Based Subsampling Methods for Big Data

plot_AMSE function

<dl><dt>object</dt>
<dd>Any object after subsampling from our subsampling function under potential model misspecification</dd></dl>

Arguments

Plotting AMSE outputs for the samples under model misspecification — plot_AMSE

<dl>

<dt>object</dt>
<dd>Any object after subsampling from our subsampling function under potential model misspecification</dd>

</dl>

plot_AMSE: Plotting AMSE outputs for the samples under model misspecification

Description

Usage

Value

Arguments

Details