00000000cam 2200000zi 4500
0019.966878
003CaOODSP
00520261016143252
006m     o  d f      
007cr |n|||||||||
008261016e20250908oncd    ob   f|0| 0 eng d
040 |aCaOODSP|beng|erda|cCaOODSP
043 |an-cn---
0861 |aCS11-522/2024-1-25E-PDF
1001 |aDagdoug, Mehdi, |eauthor.
24510|aStatistical inference for a finite population mean with machine learning-based imputation for missing survey data / |cby Mehdi Dagdoug and David Haziza.
2461 |iAt head of title: |aProceedings of Statistics Canada Symposium 2024 : |bthe future of official statistics
264 1|a[Ottawa] : |bStatistics Canada = Statistique Canada, |cSeptember 8, 2025.
300 |a1 online resource (15 unnumbered pages) : |bcharts.
336 |atext|btxt|2rdacontent
337 |acomputer|bc|2rdamedia
338 |aonline resource|bcr|2rdacarrier
4901 |a[Statistics Canada international symposium series : proceedings], |x1709-8211
500 |aIssued also in French under title: Inférence statistique pour une moyenne de population finie avec imputation fondée sur l’apprentissage automatique pour données d’enquête manquantes.
500 |aTitle from cover.
504 |aIncludes bibliographical references (pages [14-15]).
520 |a"National statistical offices have increasingly adopted machine learning (ML) for its potential to improve survey estimates. ML techniques offer significant advantages, notably the ability to manage high-dimensional data and to capture complex, nonlinear relationships, thereby enhancing the overall quality of survey statistics. In this article, following the approach of Chernozhukov et al. (2018), we describe a double debiased machine learning framework that enables valid statistical inference when imputed estimators are derived from ML procedures. Simulation results suggest that the proposed framework performs well in a wide range of scenarios"--Abstract.
650 0|aMachine learning.
650 0|aMathematical statistics|zCanada.
655 7|aStatistics|2lcgft
7102 |aStatistics Canada, |eissuing body.
77508|tInférence statistique pour une moyenne de population finie avec imputation fondée sur l’apprentissage automatique pour données d’enquête manquantes / |w(CaOODSP)9.966880
830#0|aStatistics Canada international symposium series - proceedings,|x1709-8211.|w(CaOODSP)9.505681
85640|qPDF|s527 KB|uhttps://publications.gc.ca/collections/collection_2026/statcan/11-522-x/CS11-522-2024-1-25-eng.pdf
986 |a11-522-X