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Abstract:
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Coverage intervals for a parameter estimate are frequently derived from a survey sample by assuming that the randomization-based parameter estimate is asymptotically normal and that the associated measure of the estimator's variance is roughly chi-squared. In many situations, however, the size of the sample and the nature of the parameter being estimated do not support the use of the conventional Wald technique, especially when a one-sided coverage interval is needed. We will propose a method of coverage-interval construction that "speeds up the asymptotics" so that the resulting one-sided intervals can have much better coverages than the corresponding Wald intervals while sharing the Wald intervals' large-sample randomization-based properties. For a mean or total computed from a stratified, simple random sample, no model need be assumed.
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