Measurement error arises when the observed data deviate from true values due to inaccuracies in measurement processes, potentially leading to biased estimates and ...

Measurement error in exposure assessment is unavoidable. Statistical methods to correct for such errors rely upon a valid error model, particularly regarding the ...

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 ...

We describe two approaches to instrumental variable estimation in binary regression measurement error models. The methods entail constructing approximate mean models ...

Yang, Mochen, Edward McFowland III, Gordon Burtch, and Gediminas Adomavicius. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach ...

In order to measure the mechanical properties of materials in compression, tension, bending or torsion, a universal testing machine is utilized. This article outlines the two different types ...

The Brookings Institute released a brief this week on deep poverty. The brief repeated two often cited claims: There is reason to be highly skeptical of both of these claims. When looking at ...

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 ...