Berkson error model
The Berkson error model is a description of random error (or misclassification) in measurement. Unlike classical error, Berkson error causes little or no bias in the measurement. It was proposed by Joseph Berkson in an article entitled “Are there two regressions?,”{{cite journal |last=Berkson |first=J. |title=Are There Two Regressions? |journal=Journal of the American Statistical Association |volume=45 |issue=250 |year=1950 |pages=164–180 |jstor=2280676 |doi=10.1080/01621459.1950.10483349}} published in 1950.
An example of Berkson error arises in exposure assessment in epidemiological studies. Berkson error may predominate over classical error in cases where exposure data are highly aggregated. While this kind of error reduces the power of a study, risk estimates themselves are not themselves attenuated (as would be the case where random error predominates).
References
{{reflist}}
Further reading
- {{cite book |last=Buonaccorsi |first=John P. |title=Measurement Error: Models, Methods, and Applications |publisher=CRC Press |year=2010 |isbn=978-1-4200-6658-6 |pages=76–78 |url=https://books.google.com/books?id=QVtVmaCqLHMC&pg=PA76 }}
- {{cite book |last=Carroll |first=R. J. |last2=Ruppert |first2=D. |last3=Stefanski |first3=L. A. |title=Measurement Error in Nonlinear Models |publisher=Chapman & Hall |location=London |edition=Second |year=2006 |isbn=1-4200-1013-1 |pages=26–32 |url=https://books.google.com/books?id=9kBx5CPZCqkC&pg=PA26 }}
{{DEFAULTSORT:Berkson Error Model}}
Category:Accuracy and precision
Category:Statistical deviation and dispersion
{{Statistics-stub}}