We consider quasilikelihood models when some of the predictors are measured with error. In many cases, the true but fallible predictor is impossible to measure, and ...

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

The performance of our methods is assessed both numerically and theoretically. We also suggest a fully data-driven bandwidth selection procedure, a problem that is notoriously difficult in ...

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

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.