A new class of sampling strategies is proposed that can be applied to population-based surveys ta... more A new class of sampling strategies is proposed that can be applied to population-based surveys targeting a rare trait that is unevenly spread over an area of interest. Our proposal is characterised by the ability to tailor the data collection to specific features and challenges of the survey at hand. It is based on integrating an adaptive component into a sequential selection, which aims both to intensify the detection of positive cases, upon exploiting the spatial clustering, and to provide a flexible framework to manage logistics and budget constraints. A class of estimators is also proposed to account for the selection bias, that are proved unbiased for the population mean (prevalence) as well as consistent and asymptotically Normal distributed. Unbiased variance estimation is also provided. A ready-to-implement weighting system is developed for estimation purposes. Two special strategies included in the proposed class are presented, that are based on the Poisson sampling and pro...
ABSTRACT Multiple Frame Survey has been originally proposed, according to an optimality approach,... more ABSTRACT Multiple Frame Survey has been originally proposed, according to an optimality approach, in order to persecute survey cost savings, especially in the case of a complete list available but expensive to sample. In the modern sampling practice it is frequent the case where a total and up-to-date list of units, to be used as sampling frame, is not available or it may not be built unless expensive or unfeasible screening of the target population. Instead, a set of lists singularly partial, usually overlapping, with union offering an adequate coverage of the target population, can be available. Thus the collection of the partial lists can be used as Multiple Frame. Literature about Multiple Frame estimation theory mainly concentrates over the Dual Frame case and it is only rarely concerned with the important practical issue of the variance estimation. By using a multiplicity approach a Single Frame estimator is proposed. The new estimator naturally applies to any number of frame and it is very simple so that its variance is given exactly and easily estimated.
A new class of sampling strategies is proposed that can be applied to population-based surveys ta... more A new class of sampling strategies is proposed that can be applied to population-based surveys targeting a rare trait that is unevenly spread over an area of interest. Our proposal is characterised by the ability to tailor the data collection to specific features and challenges of the survey at hand. It is based on integrating an adaptive component into a sequential selection, which aims both to intensify the detection of positive cases, upon exploiting the spatial clustering, and to provide a flexible framework to manage logistics and budget constraints. A class of estimators is also proposed to account for the selection bias, that are proved unbiased for the population mean (prevalence) as well as consistent and asymptotically Normal distributed. Unbiased variance estimation is also provided. A ready-to-implement weighting system is developed for estimation purposes. Two special strategies included in the proposed class are presented, that are based on the Poisson sampling and pro...
ABSTRACT Multiple Frame Survey has been originally proposed, according to an optimality approach,... more ABSTRACT Multiple Frame Survey has been originally proposed, according to an optimality approach, in order to persecute survey cost savings, especially in the case of a complete list available but expensive to sample. In the modern sampling practice it is frequent the case where a total and up-to-date list of units, to be used as sampling frame, is not available or it may not be built unless expensive or unfeasible screening of the target population. Instead, a set of lists singularly partial, usually overlapping, with union offering an adequate coverage of the target population, can be available. Thus the collection of the partial lists can be used as Multiple Frame. Literature about Multiple Frame estimation theory mainly concentrates over the Dual Frame case and it is only rarely concerned with the important practical issue of the variance estimation. By using a multiplicity approach a Single Frame estimator is proposed. The new estimator naturally applies to any number of frame and it is very simple so that its variance is given exactly and easily estimated.
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Papers by Fulvia Mecatti