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Bayesian Inference under Small Sample Sizes Using General Noninformative Priors

Author

Listed:
  • Jingjing He

    (School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China)

  • Wei Wang

    (School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China)

  • Min Huang

    (School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China)

  • Shaohua Wang

    (China Aviation Power Plant Research Institute, Zhuzhou 412002, China)

  • Xuefei Guan

    (Graduate School of China Academy of Engineering Physics, Beijing 100193, China)

Abstract

This paper proposes a Bayesian inference method for problems with small sample sizes. A general type of noninformative prior is proposed to formulate the Bayesian posterior. It is shown that this type of prior can represent a broad range of priors such as classical noninformative priors and asymptotically locally invariant priors and can be derived as the limiting states of normal-inverse-Gamma conjugate priors, allowing for analytical evaluations of Bayesian posteriors and predictors. The performance of different noninformative priors under small sample sizes is compared using the likelihood combining both fitting and prediction performances. Laplace approximation is used to evaluate the likelihood. A realistic fatigue reliability problem was used to illustrate the method. Following that, an actual aeroengine disk lifing application with two test samples is presented, and the results are compared with the existing method.

Suggested Citation

  • Jingjing He & Wei Wang & Min Huang & Shaohua Wang & Xuefei Guan, 2021. "Bayesian Inference under Small Sample Sizes Using General Noninformative Priors," Mathematics, MDPI, vol. 9(21), pages 1-20, November.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:21:p:2810-:d:672622
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    References listed on IDEAS

    as
    1. Zhou, Daoqing & He, Jingjing & Du, Yi-Mu & Sun, C.P. & Guan, Xuefei, 2021. "Probabilistic information fusion with point, moment and interval data in reliability assessment," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    2. Nelson, Charles R & Kim, Myung J, 1993. "Predictable Stock Returns: The Role of Small Sample Bias," Journal of Finance, American Finance Association, vol. 48(2), pages 641-661, June.
    3. James Berger & Elías Moreno & Luis Pericchi & M. Bayarri & José Bernardo & Juan Cano & Julián Horra & Jacinto Martín & David Ríos-Insúa & Bruno Betrò & A. Dasgupta & Paul Gustafson & Larry Wasserman &, 1994. "An overview of robust Bayesian analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 3(1), pages 5-124, June.
    4. Andrew Gelman & Christian Hennig, 2017. "Beyond subjective and objective in statistics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 967-1033, October.
    5. Kotz,Samuel & Nadarajah,Saralees, 2004. "Multivariate T-Distributions and Their Applications," Cambridge Books, Cambridge University Press, number 9780521826549, January.
    6. Guan, Xuefei & He, Jingjing & Jha, Ratneshwar & Liu, Yongming, 2012. "An efficient analytical Bayesian method for reliability and system response updating based on Laplace and inverse first-order reliability computations," Reliability Engineering and System Safety, Elsevier, vol. 97(1), pages 1-13.
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    1. Haohao Qu & Sheng Liu & Jun Li & Yuren Zhou & Rui Liu, 2022. "Adaptation and Learning to Learn (ALL): An Integrated Approach for Small-Sample Parking Occupancy Prediction," Mathematics, MDPI, vol. 10(12), pages 1-19, June.

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