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- Articles and reports: 12-001-X200800210757Description:
Sample weights can be calibrated to reflect the known population totals of a set of auxiliary variables. Predictors of finite population totals calculated using these weights have low bias if these variables are related to the variable of interest, but can have high variance if too many auxiliary variables are used. This article develops an "adaptive calibration" approach, where the auxiliary variables to be used in weighting are selected using sample data. Adaptively calibrated estimators are shown to have lower mean squared error and better coverage properties than non-adaptive estimators in many cases.
Release date: 2008-12-23 - Articles and reports: 82-003-X200800410747Geography: CanadaDescription:
A selective approach may be used in an ecological study where the aim is to choose a subset of units of analysis (UAs) and produce interpretations about a population of interest (PI) based solely on those UAs. The results for the PI will be reliable if that population is concentrated in the selected UAs and rare in other UAs. This article presents a graphical tool that helps determine whether these conditions are satisfied.
Release date: 2008-12-17
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- Articles and reports: 12-001-X200800210757Description:
Sample weights can be calibrated to reflect the known population totals of a set of auxiliary variables. Predictors of finite population totals calculated using these weights have low bias if these variables are related to the variable of interest, but can have high variance if too many auxiliary variables are used. This article develops an "adaptive calibration" approach, where the auxiliary variables to be used in weighting are selected using sample data. Adaptively calibrated estimators are shown to have lower mean squared error and better coverage properties than non-adaptive estimators in many cases.
Release date: 2008-12-23 - Articles and reports: 82-003-X200800410747Geography: CanadaDescription:
A selective approach may be used in an ecological study where the aim is to choose a subset of units of analysis (UAs) and produce interpretations about a population of interest (PI) based solely on those UAs. The results for the PI will be reliable if that population is concentrated in the selected UAs and rare in other UAs. This article presents a graphical tool that helps determine whether these conditions are satisfied.
Release date: 2008-12-17
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