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| 001 | 9.839753 |
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| 003 | CaOODSP |
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| 005 | 20221107151658 |
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| 007 | cr ||||||||||| |
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| 008 | 170714s2000 onc ||||os f|0| 0 eng d |
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| 040 | |aCaOODSP|beng |
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| 041 | |aeng|bfre |
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| 043 | |an-cn--- |
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| 086 | 1 |aCS92-0049/2000E-PDF |
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| 100 | 1 |aYou, Yong. |
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| 245 | 12|aA non-linear hierarchical modelling approach for census undercoverage estimation |h[electronic resource] / |cYong You. |
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| 246 | 13|aProceedings of the Survey Methods Section |
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| 260 | |a[Ottawa : |bHousehold Survey Methods Division, Statistics Canada, |c2000] |
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| 300 | |ap. 185-190 |
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| 500 | |aDigitized edition from print [produced by Statistics Canada]. |
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| 500 | |aTitle from caption. |
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| 500 | |aCopy of an article from Proceedings of the Survey Methods Section, 2000. |
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| 500 | |aIncludes abstract in French. |
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| 504 | |aIncludes bibliographical references. |
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| 520 | |a"Area-level nonlinear mixed effects models are considered in this paper for Canada census undercoverage estimation. We fit an area-level nonlinear mixed effects model to the province-level undercoverage survey estimates. In particular, the sampling model is based on the survey estimate of the undercoverage count, and the linking model is a log-linear model for the undercoverage rate. A full hierarchical Bayes (HB) approach is developed to obtain the posterior estimates of the census undercoverage using Markov Chain Monte Carlo (MCMC) sampling methods. Our result shows that the proposed method can provide efficient model-based estimates. Analysis of model fitting is also presented using posterior predictive distributions, and the corresponding result indicates that the proposed model fits the data quite well."--Abstract. |
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| 692 | 07|2gccst|aCensus |
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| 692 | 07|2gccst|aMethodology |
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| 692 | 07|2gccst|aStatistical analysis |
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| 710 | 2 |aStatistics Canada.|bMethodology Branch.|bHousehold Survey Methods Division. |
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| 856 | 40|qPDF|s552 KB|uhttps://publications.gc.ca/collections/collection_2017/statcan/CS92-0049-2000-eng.pdf |
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