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An analysis pipeline for estimating true intake from repeated measurements with random errors

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  • Seongil Jo
  • Jeongseon Kim
  • Woojoo Lee

Abstract

The accurate estimation of an individual's usual dietary intake is an important topic in nutritional epidemiology. This paper considers the best linear unbiased predictor (BLUP) computed from repeatedly measured dietary data and derives several nonparametric prediction intervals for true intake. However, the performance of the BLUP and the validity of prediction intervals depends on whether required model assumptions for the true intake estimation problem hold. To address this issue, the paper examines how the BLUP and prediction intervals behave in the case of a violation of model assumptions, and then proposes an analysis pipeline for checking them with data.

Suggested Citation

  • Seongil Jo & Jeongseon Kim & Woojoo Lee, 2019. "An analysis pipeline for estimating true intake from repeated measurements with random errors," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(5), pages 1239-1254, March.
    • Handle: RePEc:taf:lstaxx:v:48:y:2019:i:5:p:1239-1254
    DOI: 10.1080/03610926.2018.1429624
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