Survey Methodology
Design-based estimation of small and empty domains in survey data analysis using order constraints

by Xiyue Liao, Mary C. Meyer and Xiaoming XuNote 1

  • Release date: December 20, 2024
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Abstract

Recent work in survey domain estimation has shown that incorporating a priori assumptions about orderings of population domain means reduces the variance of the estimators and provides smaller confidence intervals with good coverage. Here we show how partial ordering assumptions allow design-based estimation of sample means in domains for which the sample size is zero, with conservative variance estimates and confidence intervals. Order restrictions can also substantially improve estimation and inference in small-size domains. Examples with well-known survey data sets demonstrate the utility of the methods. Code to implement the examples using the R package csurvey is given in the appendix.

Key Words:    Domain means; Isotonic; Order restrictions; R; Small area estimation; Survey.

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How to cite

Liao, X., Meyer, M.C. and Xu, X. (2024). Design-based estimation of small and empty domains in survey data analysis using order constraints. Survey Methodology, 50(2), 303-321. Paper available at http://www.statcan.gc.ca/pub/12-001-x/2024002/article/00010-eng.pdf.

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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.

© His Majesty the King in Right of Canada, as represented by the Minister of Industry, 2024

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

Catalogue No. 12-001-X

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