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Activity Number: 169 - SPEED:Improving Survey Data Quality with Multiple Data Sources, Administrative Data, and Nonresponse Bias Control
Type: Contributed
Date/Time: Monday, July 29, 2019 : 10:30 AM to 12:20 PM
Sponsor: Survey Research Methods Section
Abstract #306516
Title: A Smooth Pseudo-Population Bootstrap Approach in Survey Sampling with Applications to Quantile Estimators
Author(s): Christian Léger* and Vanessa McNealis
Companies: Université de Montréal and Université de Montréal
Keywords: Pseudo-population Approach; Variance Estimation; Median; Quantiles; Confidence Intervals; Smooth Bootstrap

The pseudo-population bootstrap approach in survey sampling consists of constructing a pseudo-population from the sampled units and to obtain a bootstrap sample using the survey design, see e.g. Booth et al. (1994). In an i.i.d. context, Hall, DiCiccio and Romano (1989) have shown that smoothing the empirical distribution function from which bootstrap samples are taken can improve variance estimates of quantile estimators.

In this paper, we extend the smooth bootstrap to the survey sampling setting. The method consists of adding i.i.d. N(0,h^2) random variables to each unit of the pseudo-population. We study the performance of the approach to construct variance estimates and confidence intervals, notably for quantile estimators. As is usually the case with smoothing methods, special care is given to the choice of the bandwidth h.

Authors who are presenting talks have a * after their name.

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