New Experimental Design Based Subsampling Methods for Big Data
A- and L-optimality criteria based subsampling under Generalised Linea...
A-optimality criteria based subsampling under Gaussian Linear Models
A-optimality criteria based subsampling under measurement constraints ...
Generate data for Generalised Linear Models
Generate data for Generalised Linear Models under model misspecificati...
Local case control sampling for logistic regression
Basic and shrinkage leverage sampling for Generalised Linear Models
Subsampling under linear regression for a potentially misspecified mod...
Subsampling under logistic regression for a potentially misspecified m...
Subsampling under Poisson regression for a potentially misspecified mo...
Model robust optimal subsampling for A- and L- optimality criteria und...
Model robust optimal subsampling for A- and L- optimality criteria und...
Model robust optimal subsampling for A- and L- optimality criteria und...
Plotting AMSE outputs for the samples under model misspecification
Plotting model parameter outputs after subsampling
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>.
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