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Maximum Likelihood Estimation of Observer Error‐Rates Using the EM Algorithm

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  • A. P. Dawid
  • A. M. Skene

Abstract

In compiling a patient record many facets are subject to errors of measurement. A model is presented which allows individual error‐rates to be estimated for polytomous facets even when the patient's “true” response is not available. The EM algorithm is shown to provide a slow but sure way of obtaining maximum likelihood estimates of the parameters of interest. Some preliminary experience is reported and the limitations of the method are described.

Suggested Citation

  • A. P. Dawid & A. M. Skene, 1979. "Maximum Likelihood Estimation of Observer Error‐Rates Using the EM Algorithm," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 28(1), pages 20-28, March.
    • Handle: RePEc:bla:jorssc:v:28:y:1979:i:1:p:20-28
    DOI: 10.2307/2346806
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    Cited by:

    1. Yuqing Kong, 2021. "Information Elicitation Meets Clustering," Papers 2110.00952, arXiv.org.
    2. Xiu Fang & Suxin Si & Guohao Sun & Quan Z. Sheng & Wenjun Wu & Kang Wang & Hang Lv, 2022. "Selecting Workers Wisely for Crowdsourcing When Copiers and Domain Experts Co-exist," Future Internet, MDPI, vol. 14(2), pages 1-22, January.
    3. Xiaoxiao Yang & Jing Zhang & Jun Peng & Lihong Lei, 2021. "Incentive mechanism based on Stackelberg game under reputation constraint for mobile crowdsensing," International Journal of Distributed Sensor Networks, , vol. 17(6), pages 15501477211, June.
    4. Jing Wang & Panagiotis G. Ipeirotis & Foster Provost, 2017. "Cost-Effective Quality Assurance in Crowd Labeling," Information Systems Research, INFORMS, vol. 28(1), pages 137-158, March.
    5. Tomer Geva & Maytal Saar‐Tsechansky, 2021. "Who Is a Better Decision Maker? Data‐Driven Expert Ranking Under Unobserved Quality," Production and Operations Management, Production and Operations Management Society, vol. 30(1), pages 127-144, January.
    6. Junming Yin & Jerry Luo & Susan A. Brown, 2021. "Learning from Crowdsourced Multi-labeling: A Variational Bayesian Approach," Information Systems Research, INFORMS, vol. 32(3), pages 752-773, September.
    7. Ahfock, Daniel & McLachlan, Geoffrey J., 2021. "Harmless label noise and informative soft-labels in supervised classification," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    8. Jesus Cerquides & Mehmet Oğuz Mülâyim & Jerónimo Hernández-González & Amudha Ravi Shankar & Jose Luis Fernandez-Marquez, 2021. "A Conceptual Probabilistic Framework for Annotation Aggregation of Citizen Science Data," Mathematics, MDPI, vol. 9(8), pages 1-15, April.
    9. Alaa Ghanaiem & Evgeny Kagan & Parteek Kumar & Tal Raviv & Peter Glynn & Irad Ben-Gal, 2023. "Unsupervised Classification under Uncertainty: The Distance-Based Algorithm," Mathematics, MDPI, vol. 11(23), pages 1-19, November.

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