Active sampling: A machine-learning-assisted framework for finite population inference with optimal subsamples-Reference-Cited by-同舟云学术

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Active sampling: A machine-learning-assisted framework for finite population inference with optimal subsamples

Published:2024-07-02 Issue: Volume: Page:1-23
ISSN:0040-1706
Container-title:Technometrics
language:en
Short-container-title:Technometrics

Author:

Imberg Henrik¹^ORCID,Yang Xiaomi²^ORCID,Flannagan Carol³,Bärgman Jonas²

Affiliation:

1. Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg

2. Division of Vehicle Safety, Chalmers University of Technology

3. University of Michigan Transportation Research Institute, Division of Vehicle Safety, Chalmers University of Technology

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/00401706.2024.2374554

Reference70 articles.

1. Ai M. Wang F. Yu J. and Zhang H. (2021a). Optimal subsampling for large-scale quantile regression. Journal of Complexity 62:101512.

2. Ai M. Yu J. Zhang H. and Wang H. (2021b). Optimal subsampling algorithms for big data regressions. Statistica Sinica 31:749–772.

3. Bach F. R. (2007). Active learning for misspecified generalized linear models. In Advances in Neural Information Processing Systems 19.

4. Bärgman J. Lisovskaja V. Victor T. Flannagan C. and Dozza M. (2015). How does glance behavior influence crash and injury risk? A ‘what-if’ counterfactual simulation using crashes and near-crashes from SHRP2. Transportation Research Part F: Traffic Psychology and Behaviour 35:152–169.

5. Batsch F. Daneshkhah A. Palade V. and Cheah M. (2021). Scenario optimisation and sensitivity analysis for safe automated driving using Gaussian processes. Applied Sciences 11(2):775.

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