JSM 2004 - Toronto

Abstract #300227

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Activity Number: 422
Type: Invited
Date/Time: Thursday, August 12, 2004 : 10:30 AM to 12:20 PM
Sponsor: Section on Survey Research Methods
Abstract - #300227
Title: Outlier Treatment for Disaggregated Estimates
Author(s): Louis-Paul Rivest*+ and Mike Hidiroglou
Companies: Université Laval and Office of National Statistics
Address: Department of Mathematics and Statistics, Ste-Foy, PQ, G1K 7P4, Canada
Keywords: outliers ; Winsorization ; skew distributions ; efficiency comparisons ; bias ; mean squared error
Abstract:

In many surveys the total sample is large enough for outliers to have a negligible impact on aggregated estimates. Their contributions can still be substantial at disaggregated levels, for domain estimation. In surveys repeated over time, yearly or biennial estimates are typically outlier-resistant, while monthly or quarterly estimates can vary a lot when outliers occur. This presentation suggests methods to reduce the impact of outliers on disaggregated estimates while keeping aggregated estimates unchanged. The proposed method is akin to using a "Surprise Stratum" as proposed by Leslie Kish in his 1965 book, Survey Sampling. It is implemented for a stratified random sampling plan, where the objective is to reduce the impact of outliers on stratum estimates while keeping the population estimate unchanged. In each stratum a winsorization cut-off is set; the stratum estimate is given by the stratum winsorized estimate plus the stratum share of the data values exceeding their stratum cut-offs.

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