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Bayesian linear models for cardinal paired comparison data

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  • Osei, Prince P.
  • Davidov, Ori

Abstract

This paper develops a methodology for Bayesian updating in normal linear models in situations where the parameter of interest is restricted to a linear subspace. The methodology is motivated by and applied to the calculation of posterior distributions for the merit parameters and ranks arising in paired comparison data. The Bayesian paradigm is found to be ideal for assessing and quantifying the uncertainty in ranking procedures. The methodology is illustrated using simulated data and applied to two data sets: a network meta–analysis example and to the ranking of teams in the National Basketball Association (NBA).

Suggested Citation

  • Osei, Prince P. & Davidov, Ori, 2022. "Bayesian linear models for cardinal paired comparison data," Computational Statistics & Data Analysis, Elsevier, vol. 172(C).
    • Handle: RePEc:eee:csdana:v:172:y:2022:i:c:s0167947322000615
    DOI: 10.1016/j.csda.2022.107481
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    References listed on IDEAS

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