Kernel-Based Bootstrap Synthetic Data to Estimate Measurement Uncertainty in Analytical Sciences

Abstract

Measurement uncertainty (MU) is becoming a key figure of merit for analytical methods, and estimating MU from method validation data is cost-effective and practical. Since MU can be defined as a coverage interval of a given result, the computation of statistical prediction intervals is a possible approach, but the quality of the intervals is questionable when the number of available data is reduced. In this context, the bootstrap procedure constitutes an efficient strategy to increase the observed data variability. While applying naive bootstrap to validation data raises some computational challenges, the use of smooth bootstrap is much more interesting when synthetic data are generated using an adapted kernel density estimation algorithm. MU can be directly obtained in a very convenient way as an uncertainty function applicable to any unknown future measurement. This publication presents the advantages and disadvantages of this new method illustrated using diverse in-house and interlaboratory validation data.