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SUFFICIENT SAMPLE SIZE: LIKELIHOOD BOOTTRAPPING
- Authors: Kiselev N.S1, Grabovoi A.V1
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Affiliations:
- MIPT
- Issue: Vol 65, No 2 (2025)
- Pages: 235-242
- Section: Computer science
- URL: https://journals.rcsi.science/0044-4669/article/view/287399
- DOI: https://doi.org/10.31857/S0044466925020094
- EDN: https://elibrary.ru/CBDKTA
- ID: 287399
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Abstract
Determining the appropriate sample size is crucial for building effective machine learning models. Existing methods often either lack a rigorous theoretical basis or are tied to specific statistical hypotheses about the model parameters. In this paper, we present two new methods based on likelihood values on bootstrapped subsamples. We demonstrate the correctness of one of these methods in a linear regression model. Computational experiments with both synthetic and real datasets show that the proposed functions converge as the sample size increases, highlighting the practical usefulness of the approach.
About the authors
N. S Kiselev
MIPT
Email: kiselev.ns@phystech.edu
Dolgoprudny, Russia
A. V Grabovoi
MIPT
Email: grabovoy.av@phystech.edu
Dolgoprudny, Russia
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