Analysis

All (2) ((2 results))

• 1. A method to find an efficient and robust sampling strategy under model uncertainty

  Articles and reports: 12-001-X202100100002

  Description:

  We consider the problem of deciding on sampling strategy, in particular sampling design. We propose a risk measure, whose minimizing value guides the choice. The method makes use of a superpopulation model and takes into account uncertainty about its parameters through a prior distribution. The method is illustrated with a real dataset, yielding satisfactory results. As a baseline, we use the strategy that couples probability proportional-to-size sampling with the difference estimator, as it is known to be optimal when the superpopulation model is fully known. We show that, even under moderate misspecifications of the model, this strategy is not robust and can be outperformed by some alternatives.

  Release date: 2021-06-24

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• 2. Feeding back information on ineligibility from sample surveys to the frame Archived

  Articles and reports: 12-001-X20040027756

  Description:

  It is usually discovered in the data collection phase of a survey that some units in the sample are ineligible even if the frame information has indicated otherwise. For example, in many business surveys a nonnegligible proportion of the sampled units will have ceased trading since the latest update of the frame. This information may be fed back to the frame and used in subsequent surveys, thereby making forthcoming samples more efficient by avoiding sampling ineligible units. On the first of two survey occasions, we assume that all ineligible units in the sample (or set of samples) are detected and excluded from the frame. On the second occasion, a subsample of the eligible part is observed again. The subsample may be augmented with a fresh sample that will contain both eligible and ineligible units. We investigate what effect on survey estimation the process of feeding back information on ineligibility may have, and derive an expression for the bias that can occur as a result of feeding back. The focus is on estimation of the total using the common expansion estimator. An estimator that is nearly unbiased in the presence of feed back is obtained. This estimator relies on consistent estimates of the number of eligible and ineligible units in the population being available.

  Release date: 2005-02-03

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

• 1. A method to find an efficient and robust sampling strategy under model uncertainty

  Articles and reports: 12-001-X202100100002

  Description:

  We consider the problem of deciding on sampling strategy, in particular sampling design. We propose a risk measure, whose minimizing value guides the choice. The method makes use of a superpopulation model and takes into account uncertainty about its parameters through a prior distribution. The method is illustrated with a real dataset, yielding satisfactory results. As a baseline, we use the strategy that couples probability proportional-to-size sampling with the difference estimator, as it is known to be optimal when the superpopulation model is fully known. We show that, even under moderate misspecifications of the model, this strategy is not robust and can be outperformed by some alternatives.

  Release date: 2021-06-24

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• 2. Feeding back information on ineligibility from sample surveys to the frame Archived

  Articles and reports: 12-001-X20040027756

  Description:

  It is usually discovered in the data collection phase of a survey that some units in the sample are ineligible even if the frame information has indicated otherwise. For example, in many business surveys a nonnegligible proportion of the sampled units will have ceased trading since the latest update of the frame. This information may be fed back to the frame and used in subsequent surveys, thereby making forthcoming samples more efficient by avoiding sampling ineligible units. On the first of two survey occasions, we assume that all ineligible units in the sample (or set of samples) are detected and excluded from the frame. On the second occasion, a subsample of the eligible part is observed again. The subsample may be augmented with a fresh sample that will contain both eligible and ineligible units. We investigate what effect on survey estimation the process of feeding back information on ineligibility may have, and derive an expression for the bias that can occur as a result of feeding back. The focus is on estimation of the total using the common expansion estimator. An estimator that is nearly unbiased in the presence of feed back is obtained. This estimator relies on consistent estimates of the number of eligible and ineligible units in the population being available.

  Release date: 2005-02-03

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