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Monday, June 14
Mon, Jun 14, 10:30 AM - 12:00 PM
Robust Estimation in the Presence of Influential Units

Bootstrap Estimation of the Conditional Bias for Measuring Influence in Complex Surveys (307931)

*Jean-Francois Beaumont, Statistics Canada 

Keywords: Bootstrap, Conditional bias, Complex survey, Influence measure

In sample surveys that collect information on skewed variables, it is often desirable to assess the influence of sample units on the sampling error of commonly-used estimators. The conditional bias, initially proposed by Moreno-Rebollo, Munoz-Reyez and Munoz-Pichardo (1999), is an attractive measure of influence that accounts for the sampling design and the estimation method. It is defined as the design expectation of the sampling error conditional on a given unit being selected in the sample. The estimation of the conditional bias is relatively straightforward for simple sampling designs and estimators. However, for complex designs or complex estimators, it may be tedious to derive an explicit expression for the conditional bias. In those complex surveys, variance estimation is often achieved through replication methods such as the bootstrap. Bootstrap methods are typically implemented by producing a set of bootstrap weights that is made available to users along with the survey data. In this talk, we show how to use these available bootstrap weights to obtain an estimator of the conditional bias. Our bootstrap estimator is evaluated in a simulation study.