JSM 2004 - Toronto

Abstract #301525

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Activity Number: 426
Type: Contributed
Date/Time: Thursday, August 12, 2004 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #301525
Title: Outlier Detection and Treatment in the Current Employment Statistics Survey
Author(s): Julie B. Gershunskaya*+ and Larry L. Huff
Companies: Bureau of Labor Statistics and Bureau of Labor Statistics
Address: 2 Massachusetts Ave. NE, Washington, DC, 20212,
Keywords: robust estimation ; one-step M-estimator ; ratio estimator ; influence curve
Abstract:

The Current Employment Statistics (CES) Survey uses a weighted link relative estimator to make estimates of employment at various levels of industry and area detail. The estimates are produced monthly approximately three weeks after the reference date of the survey. Sometimes outliers combined with relatively large probability weights result in influential reporters that cause estimates of smaller domains to be very unstable. An employment figure reported to the survey may be considered typical for a relatively large estimation domain; however, it may be unusual and highly influential for a more detailed industry and area domain. The focus of the current simulation study is to explore feasibility of using a robust estimation technique in a simple and automated way to detect and treat outliers during the short time frame allotted for monthly survey processing. Results are evaluated based on the deviation of the estimates from the true population levels.

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