Nonlinear models of measurement errors are used to analyze and correct inaccuracies in data collection and interpretation.

For many variables measured in epidemiology, particularly those based on self-report, however, errors are often correlated with the true value, and these rules may not apply.

Furthermore, developments in nonparametric identification have facilitated more flexible specifications in models confronting errors in variables, enabling robust applications in fields such as ...

Material testing requires a knowledge of how the factors of accuracy, repeatability and resolution. Find out more about measurement errors.

Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling ...

For estimates among U.S. adults on 28 benchmark variables, opt-in samples 1, 2 and 3 had average absolute errors of 6.4, 6.1 and 5.0, respectively, for an overall average of 5.8 percentage points.

It supports the two-point calibration practice to minimize errors, particularly at lower concentrations, and recommends measures such as improved aerosol systems and automation to enhance calibration ...