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Two Error Components Model for Measurement Error: Application to Radon

In this paper, a simple model for analysing variability in radon concentrations in homes is tested. The
approach used here involves two error components, representing additive and multiplicative errors,
together with variation between-houses. We use a Bayesian approach for our analysis and apply this
model to two datasets of repeat radon measurements in homes; one based on 3-month long
measurements for which the original measurements were close to the current UK Radon Action Level
(200 Bq m3), and the other based on 6-month measurement data (from regional and national surveys),
for which the original measurements cover a wide range of radon concentrations, down to very low
levels. The model with two error components provides a better fit to these datasets than does a model
based on solely multiplicative errors.

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