Survey Methodology
Robust variance estimators for generalized regression estimators in cluster samples

by Timothy L. Kennel and Richard ValliantNote 1

  • Release date: December 17, 2019
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Abstract

Standard linearization estimators of the variance of the general regression estimator are often too small, leading to confidence intervals that do not cover at the desired rate. Hat matrix adjustments can be used in two-stage sampling that help remedy this problem. We present theory for several new variance estimators and compare them to standard estimators in a series of simulations. The proposed estimators correct negative biases and improve confidence interval coverage rates in a variety of situations that mirror ones that are met in practice.

Key Words:      Jackknife variance estimator; Hat matrix adjustment; Leverage adjustment; Superpopulation model; Two-stage sample; Sandwich variance estimator.

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How to cite

Kennel, T.L., and Valliant, R. (2019). Robust variance estimators for generalized regression estimators in cluster samples. Survey Methodology, Statistics Canada, Catalogue No. 12-001-X, Vol. 45, No. 3. Paper available at http://www.statcan.gc.ca/pub/12-001-x/2019003/article/00001-eng.htm.

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ISSN : 1492-0921

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Survey Methodology publishes articles dealing with various aspects of statistical development relevant to a statistical agency, such as design issues in the context of practical constraints, use of different data sources and collection techniques, total survey error, survey evaluation, research in survey methodology, time series analysis, seasonal adjustment, demographic studies, data integration, estimation and data analysis methods, and general survey systems development. The emphasis is placed on the development and evaluation of specific methodologies as applied to data collection or the data themselves. All papers will be refereed. However, the authors retain full responsibility for the contents of their papers and opinions expressed are not necessarily those of the Editorial Board or of Statistics Canada.

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Published by authority of the Minister responsible for Statistics Canada.

© Her Majesty the Queen in Right of Canada as represented by the Minister of Industry, 2019

Use of this publication is governed by the Statistics Canada Open Licence Agreement.

Catalogue No. 12-001-X

Frequency: Semi-annual

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Table of contents

Abstract - Article main page
Section 1. Introduction
Section 2. Theoretical results
Section 3. Simulation
Section 4. Conclusion
Acknowledgements
Appendix
References

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