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An Overview of Normal Theory Structural Measurement Error Models

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  • Jeffrey R. Thompson
  • Randy L. Carter

Abstract

This paper gives an introduction and overview to the often under‐used measurement error model. The purpose is to provide a simple summary of problems that arise from measurement error and of the solutions that have been proposed. We start by describing how measurement error models occur in real‐world situations. Then we proceed with defining the measurement error model, initially introducing the multivariate form of the model, and then, starting with the simplest form of the model thoroughly discuss its features and solutions to the problems introduced due to measurement error. We discuss higher‐dimensional and more advanced forms of the model and give a brief numerical illustration. Cet article donne une introduction et une vue d'ensemble au modèle souvent sous‐utilisé d'erreur de mesure. Le but est de fournir un résumé simple des problèmes qui surgissent de l'erreur de mesure et des solutions qui ont été proposées. Nous commençons par décrire comment les modèles d'erreur de mesure se produisent dans des situations réelles. Alors nous continuons de définir le modèle d'erreur de mesure, présentant au commencement la forme multivariable du modèle, et puis, commençant par la forme la plus simple du modèle discutez complètement ses dispositifs et solutions à l'en raison présenté par problèmes de l'erreur de mesure. Nous discutons des formes dimensionnelles et plus avançées plus élevées du modèle et donnons une brève illustration numérique.

Suggested Citation

  • Jeffrey R. Thompson & Randy L. Carter, 2007. "An Overview of Normal Theory Structural Measurement Error Models," International Statistical Review, International Statistical Institute, vol. 75(2), pages 183-198, August.
  • Handle: RePEc:bla:istatr:v:75:y:2007:i:2:p:183-198
    DOI: 10.1111/j.1751-5823.2007.00014.x
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    Cited by:

    1. Liang Yan & Rui Wang & Xingzhong Xu, 2017. "Fiducial inference in the classical errors-in-variables model," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 80(1), pages 93-114, January.
    2. Liang Yan & Rui Wang & Xingzhong Xu, 2017. "A new confidence interval in errors-in-variables model with known error variance," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(12), pages 2204-2221, September.
    3. Mengli Zhang & Yang Bai, 2021. "On the use of repeated measurement errors in linear regression models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(5), pages 779-803, July.

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