Measurement Error

Jeffrey S. Buzas, Leonard A. Stefanski and Tor D. Tosteson
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Jeffrey S. Buzas: University of Vermont, Department of Mathematics and Statistics
Leonard A. Stefanski: State University, Department of Statistics North Carolina
Tor D. Tosteson: Dartmouth Medical School

Chapter II.5 in Handbook of Epidemiology, 2005, pp 729-765 from Springer

Abstract: Abstract Factors contributing to the presence or absence of disease are not always easily determined or accurately measured. Consequently epidemiologists are often faced with the task of inferring disease patterns using noisy or indirect measurements of risk factors or covariates. Problems of measurement arise for a number of reasons, including for example: reliance on self-reported information; the use of records of suspect quality; intrinsic biological variability; sampling variability; and laboratory analysis error. Although the reasons for imprecise measurement are diverse, the inference problems they create share in common the structure that statistical models must be fit to data formulated in terms of well-defined but unobservable variables X, using information on measurements W that are less than perfectly correlated with X. Problems of this nature are called measurement error problems and the statistical models and methods for analyzing such data are called measurement error models.

Keywords: Corrected Score; Measurement Error Model; Measurement Error Variance; Regression Calibration; Naive Estimator (search for similar items in EconPapers)
Date: 2005
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DOI: 10.1007/978-3-540-26577-1_19

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