Survey Methodology
Comments by David HazizaNote 1 on “Trends and directions in sample survey theory and methods”

  • Release date: June 30, 2025
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Abstract

This discussion of the paper by Rao and Lohr focuses on the use of machine learning procedures for estimating finite population parameters. While there is growing interest in these methods within national statistical offices, several areas remain largely unexplored and warrant significant attention in the coming years. In this discussion, I highlight potential topics for future research and development in this rapidly evolving field.

Key Words:      Finite population parameter; Imputation; Model-assisted estimation; Prediction; Propensity score estimation; Small area estimation; Statistical inference.

How to cite

Haziza, D. (2025). Comments on “Trends and directions in sample survey theory and methods”. Survey Methodology, 51(1), 115-123. Paper available at http://www.statcan.gc.ca/pub/12-001-x/2025001/article/00013-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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© His Majesty the King in Right of Canada, as represented by the Minister of Industry, 2025

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Catalogue No. 12-001-X

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