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  • 1. Tribute to Chris Skinner, a colleague and friend
    Articles and reports: 12-001-X202300100008
    Description: This brief tribute reviews Chris Skinner’s main scientific contributions.
    Release date: 2023-06-30
  • 2. Sampling with adaptive drawing probabilities
    Articles and reports: 12-001-X202300100009
    Description: In this paper, with and without-replacement versions of adaptive proportional to size sampling are presented. Unbiased estimators are developed for these methods and their properties are studied. In the two versions, the drawing probabilities are adapted during the sampling process based on the observations already selected. To this end, in the version with-replacement, after each draw and observation of the variable of interest, the vector of the auxiliary variable will be updated using the observed values of the variable of interest to approximate the exact selection probability proportional to size. For the without-replacement version, first, using an initial sample, we model the relationship between the variable of interest and the auxiliary variable. Then, utilizing this relationship, we estimate the unknown (unobserved) population units. Finally, on these estimated population units, we select a new sample proportional to size without-replacement. These approaches can significantly improve the efficiency of designs not only in the case of a positive linear relationship, but also in the case of a non-linear or negative linear relationship between the variables. We investigate the efficiencies of the designs through simulations and real case studies on medicinal flowers, social and economic data.
    Release date: 2023-06-30
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Articles and reports (2)

Articles and reports (2) ((2 results))

  • 1. Tribute to Chris Skinner, a colleague and friend
    Articles and reports: 12-001-X202300100008
    Description: This brief tribute reviews Chris Skinner’s main scientific contributions.
    Release date: 2023-06-30
  • 2. Sampling with adaptive drawing probabilities
    Articles and reports: 12-001-X202300100009
    Description: In this paper, with and without-replacement versions of adaptive proportional to size sampling are presented. Unbiased estimators are developed for these methods and their properties are studied. In the two versions, the drawing probabilities are adapted during the sampling process based on the observations already selected. To this end, in the version with-replacement, after each draw and observation of the variable of interest, the vector of the auxiliary variable will be updated using the observed values of the variable of interest to approximate the exact selection probability proportional to size. For the without-replacement version, first, using an initial sample, we model the relationship between the variable of interest and the auxiliary variable. Then, utilizing this relationship, we estimate the unknown (unobserved) population units. Finally, on these estimated population units, we select a new sample proportional to size without-replacement. These approaches can significantly improve the efficiency of designs not only in the case of a positive linear relationship, but also in the case of a non-linear or negative linear relationship between the variables. We investigate the efficiencies of the designs through simulations and real case studies on medicinal flowers, social and economic data.
    Release date: 2023-06-30
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