On Model-Failure When Estimating from Cutoff Samples
by James R. Knaub, Jr.
A cutoff sample may generally be considered only because it is easy to administer and relatively inexpensive. It is not often recognized that a cutoff sample may also be the option providing the smallest total survey error (TSE). Consider that model-assisted design-based sampling adjusts for samples drawn at random to compensate for the fact that the mean of the random sample can vary greatly from the mean of the population. Thus the importance of regression models in survey statistics is recognized. For cutoff sampling, accuracy may be improved by predicting for many of the ‘small’ cases that may not be able to report accurately on a frequent basis. Survey resources may then be used to concentrate on data collection for the largest possible observations. There are considerations that may mitigate the impact of model-failure with respect to estimating for the cases where there is no chance of sample selection. This article emphasizes those mitigating conditions.
Total Survey Error, bias and variance from sampling error, bias, and variance from nonsampling error, model error decomposition, performance measures
James R. Knaub, Jr, James.Knaub@eia.gov
Richard G. Graf, email@example.com
This article was revised on 1/5/11.
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