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Foreword to the special issue on “Survey Methods for Statistical Data Integration and New Data Sources”

Author

Listed:
  • M. Giovanna Ranalli

    (University of Perugia)

  • Jean-François Beaumont

    (Statistics Canada)

  • Gaia Bertarelli

    (Università Ca’ Foscari)

  • Natalie Shlomo

    (University of Manchester)

Abstract

No abstract is available for this item.

Suggested Citation

  • M. Giovanna Ranalli & Jean-François Beaumont & Gaia Bertarelli & Natalie Shlomo, 2023. "Foreword to the special issue on “Survey Methods for Statistical Data Integration and New Data Sources”," METRON, Springer;Sapienza Università di Roma, vol. 81(1), pages 1-3, April.
    • Handle: RePEc:spr:metron:v:81:y:2023:i:1:d:10.1007_s40300-023-00248-1
    DOI: 10.1007/s40300-023-00248-1
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    References listed on IDEAS

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    1. Roberta Varriale & Marco Alfo’, 2023. "Multi-source statistics on employment status in Italy, a machine learning approach," METRON, Springer;Sapienza Università di Roma, vol. 81(1), pages 37-63, April.
    2. Ian E. Fellows & Mark S. Handcock, 2023. "Modeling of networked populations when data is sampled or missing," METRON, Springer;Sapienza Università di Roma, vol. 81(1), pages 21-35, April.
    3. Chien-Min Huang & F. Jay Breidt, 2023. "A dual-frame approach for estimation with respondent-driven samples," METRON, Springer;Sapienza Università di Roma, vol. 81(1), pages 65-81, April.
    4. Andreas Quatember, 2023. "Efficient item count techniques with one or two lists," METRON, Springer;Sapienza Università di Roma, vol. 81(1), pages 5-19, April.
    5. Fabrizio Solari & Antonella Bernardini & Nicoletta Cibella, 2023. "Statistical framework for fully register based population counts," METRON, Springer;Sapienza Università di Roma, vol. 81(1), pages 109-129, April.
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