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  • 1.
    Grafström, Anton
    et al.
    Swedish Univ Agr Sci, Umeå.
    Ekström, Magnus
    Swedish Univ Agr Sci, Umeå; Umeå Univ, Umeå.
    Jonsson, Bengt-Gunnar
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Natural Sciences.
    Esseen, Per-Anders
    Umeå Univ, Umeå.
    Stahl, Göran
    Swedish Univ Agr Sci, Umeå.
    On combining independent probability samples2019In: Survey Methodology, ISSN 0714-0045, E-ISSN 1492-0921, Vol. 45, no 2, p. 349-364Article in journal (Refereed)
    Abstract [en]

    Merging available sources of information is becoming increasingly important for improving estimates of population characteristics in a variety of fields. In presence of several independent probability samples from a finite population we investigate options for a combined estimator of the population total, based on either a linear combination of the separate estimators or on the combined sample approach. A linear combination estimator based on estimated variances can be biased as the separate estimators of the population total can be highly correlated to their respective variance estimators. We illustrate the possibility to use the combined sample to estimate the variances of the separate estimators, which results in general pooled variance estimators. These pooled variance estimators use all available information and have potential to significantly reduce bias of a linear combination of separate estimators.

  • 2.
    Grafström, Anton
    et al.
    SLU, Umeå.
    Ekström, Magnus
    Umeå universitet, Umeå; SLU, Umeå.
    Jonsson, Bengt-Gunnar
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Natural Sciences.
    Esseen, Per-Anders
    Umeå universitet, Umeå.
    Ståhl, Göran
    SLU, Umeå.
    On combining independent probability samples2019In: Survey Methodology, ISSN 0714-0045, E-ISSN 1492-0921, Vol. 45, no 2, p. 371-387Article in journal (Refereed)
    Abstract [en]

    Merging available sources of information is becoming increasingly important for improving estimates ofpopulation characteristics in a variety of fields. In presence of several independent probability samples from afinite population we investigate options for a combined estimator of the population total, based on either a linearcombination of the separate estimators or on the combined sample approach. A linear combination estimatorbased on estimated variances can be biased as the separate estimators of the population total can be highlycorrelated to their respective variance estimators. We illustrate the possibility to use the combined sample toestimate the variances of the separate estimators, which results in general pooled variance estimators. Thesepooled variance estimators use all available information and have potential to significantly reduce bias of a linearcombination of separate estimators.

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  • apa
  • ieee
  • modern-language-association-8th-edition
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  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
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  • Other locale
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