Multivariate Calibration When the Error Covariance Matrix Is Structured

This article concerns multivariate calibration in linear models. The error covariance matrix is assumed to have linear factor structure. A new approach to calibration incorporating distributional properties of the observations of the calibration set is proposed. The proposed predictor is based on estimating the parameters in the best linear predictor in the assumed model. The predictor is tested on two data sets: meat data and fish data.