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Matching and Prediction on the Principle of Biological Classification

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  • William A. Belson

Abstract

In this article Dr Belson describes a technique for matching population samples. This depends upon the combination of empirically developed predictors to give the best available predictive, or matching, composite. The underlying principle is quite distinct from that inherent in the multiple correlation method.

Suggested Citation

  • William A. Belson, 1959. "Matching and Prediction on the Principle of Biological Classification," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 8(2), pages 65-75, June.
    • Handle: RePEc:bla:jorssc:v:8:y:1959:i:2:p:65-75
    DOI: 10.2307/2985543
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    Cited by:

    1. Hache, Emmanuel & Leboullenger, Déborah & Mignon, Valérie, 2017. "Beyond average energy consumption in the French residential housing market: A household classification approach," Energy Policy, Elsevier, vol. 107(C), pages 82-95.
    2. Piero Demetrio Falorsi & Salvatore Filiberti & Antonio Pavone, 2006. "The new strategy for the concise presentation of sampling errors in the Italian Structural Business Statistics Survey," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 15(2), pages 243-265, August.
    3. Athanasios Anagnostis & Serafeim Moustakidis & Elpiniki Papageorgiou & Dionysis Bochtis, 2022. "A Hybrid Bimodal LSTM Architecture for Cascading Thermal Energy Storage Modelling," Energies, MDPI, vol. 15(6), pages 1-24, March.
    4. Benjamin M. Abdel-Karim & Nicolas Pfeuffer & Oliver Hinz, 2021. "Machine learning in information systems - a bibliographic review and open research issues," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 643-670, September.
    5. Sahar Ahmadzadeh & Tahmina Ajmal & Ramakrishnan Ramanathan & Yanqing Duan, 2023. "A Comprehensive Review on Food Waste Reduction Based on IoT and Big Data Technologies," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    6. Piero Falorsi & Salvatore Filiberti & Antonio Pavone, 2006. "The new strategy for the concise presentation of sampling errors in the Italian Structural Business Statistics Survey," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 15(2), pages 243-265, August.
    7. Bernard Dushimimana & Yvonne Wambui & Timothy Lubega & Patrick E. McSharry, 2020. "Use of Machine Learning Techniques to Create a Credit Score Model for Airtime Loans," JRFM, MDPI, vol. 13(8), pages 1-11, August.

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