Coverage intervals for a parameter estimate computed using complex survey data are often constructed by assuming the parameter estimate has an asymptotically normal distribution and the measure of the estimator’s variance is roughly chi-squared. The size of the sample and the nature of the parameter being estimated render this conventional “Wald” methodology dubious in many applications. I developed a revised method of coverage-interval construction that “speeds up the asymptotics” by incorporating an estimated measure of skewness. I discuss how skewness-adjusted intervals can be computed for ratios, differences between domain means, and regression coefficients.
Better coverage intervals for estimators from a complex sample survey
By Phillip Samuel Kott.
February 2020 Open Access Peer Reviewed
Kott, P. S. (2020). Better coverage intervals for estimators from a complex sample survey. RTI Press. RTI Press Publication No. MR-0041-2002 https://doi.org/10.3768/rtipress.2020.mr.0041.2002
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