Combining univariate calibration information through a mixed-effects model

✍ Scribed by Jason J. Z. Liao

• Publisher: John Wiley and Sons
• Year: 2003
• Tongue: English
• Weight: 155 KB
• Volume: 17
• Category: Article
• ISSN: 0886-9383
• DOI: 10.1002/cem.769

✦ Synopsis

Abstract

It is common practice to calibrate a common value by combining information from different sources such as days, people, instruments and laboratories. Under each individual source a univariate calibration can be used to calibrate the unknown. Then the common unknown can be estimated by combining the estimates from each source as a weighted mean (Johnson DJ, Krishnamoorthy K. J. Am. Statist. Assoc. 1996; 91: 1707–1715) or through a multivariate calibration setting by combining information first and then estimating the common value (Liao JJZ. J. Chemometrics 2001; 15: 789–794). In this paper a mixed‐effects model approach is proposed to combine good characteristics from both approaches. Simulations show that the mixed‐effects model has better bias and mean squared error (MSE) performance than the univariate and multivariate approaches. A real data set is used to demonstrate the good characteristics of the mixed‐effects model approach. Copyright © 2003 John Wiley & Sons, Ltd.