Model updating techniques have to handle several difficulties, like incomplete experimental data, measurement errors, non-unique solutions and modeling uncertainties.
However, data are never uncertainty-free and a suitable approach is needed to face data measurement errors and their intrinsic randomness in problems with well-established physical constraints.
The debate on measurement issues in social science over the last few decades has been mainly on advanced methodologies such as path analysis and structural equation modeling to address concerns of data collection, measurement error, and ordinal data (1– 3).
One might argue that the principal reason for such attenuation is increased misclassification and mismeasurement of exposure at high levels, in which case it might make more sense to attempt to correct the exposure response data for measurement error than to model mismeasured data.
As a result, using the data without measurement error, the location and cause of abnormality can be exactly identified.
On the other hand, using the data with measurement error, the location of abnormality can be estimated in the case of accurate measurement, and the cause of abnormality can be identified with sufficient accuracy.
These cases also emerge in some prior work with quadratic adjustment costs (e.g. Hall (2004)) and may result from a combination of remaining outliers in the data and measurement error.
Measurements of training volume in the logbook as well as the Falls Efficacy Scale questionnaire are patient-collected data distributing measurement error equally in both groups.
The selected model was validated regarding the robustness of the inferred network against small changes in the time series data, reflecting measurement error due to technical or biological variance.
Clinical incomparability can be caused by differences in the VISN level populations, and design incomparability can be caused due to differences, for example, in missing data and measurement error.
