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Sample size determination: posterior distributions proximity

Author

Listed:
  • Nikita Kiselev

    (Moscow Institute of Physics and Technology)

  • Andrey Grabovoy

    (Moscow Institute of Physics and Technology)

Abstract

The issue of sample size determination is crucial for constructing an effective machine learning model. However, the existing methods for determining a sufficient sample size are either not strictly proven, or relate to the specific statistical hypothesis about the distribution of model parameters. In this paper we present two approaches based on the proximity of posterior distributions of model parameters on similar subsamples. We show that these two methods are valid for the model with normal posterior distribution of parameters. Computational experiments demonstrate the convergence of the proposed functions as the sample size increases. We also compare the proposed methods with other approaches on different datasets.

Suggested Citation

  • Nikita Kiselev & Andrey Grabovoy, 2025. "Sample size determination: posterior distributions proximity," Computational Management Science, Springer, vol. 22(1), pages 1-16, June.
    • Handle: RePEc:spr:comgts:v:22:y:2025:i:1:d:10.1007_s10287-024-00528-9
    DOI: 10.1007/s10287-024-00528-9
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    References listed on IDEAS

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    1. Pierpaolo Brutti & Fulvio Santis & Stefania Gubbiotti, 2014. "Bayesian-frequentist sample size determination: a game of two priors," METRON, Springer;Sapienza Università di Roma, vol. 72(2), pages 133-151, August.
    2. Gwowen Shieh, 2000. "On Power and Sample Size Calculations for Likelihood Ratio Tests in Generalized Linear Models," Biometrics, The International Biometric Society, vol. 56(4), pages 1192-1196, December.
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