Mixtures of Berkson and classical covariate measurement error in the linear mixed model: Bias analysis and application to a study on ultrafine particles is a research paper published in Biometrical Journal (2018). On theSindex it has a DataRank of 0.416. It has been cited 15 times.
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Base Score Contribution
0.416
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →Deutsche Forschungsgemeinschaft
Grant: KU1359/2‐1
Deutsche Forschungsgemeinschaft
Grant: unidentified
unidentified
FWCI
1.14
Citation Percentile
0.7%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Additional file 1 of Quantifying the short-term effects of air pollution on health in the presence of exposure measurement error: a simulation study of multi-pollutant model results
Additional file 1 of Quantifying the short-term effects of air pollution on health in the presence of exposure measurement error: a simulation study of multi-pollutant model results