Uncertainty estimation for multivariate regression coefficients

Five methods are compared for assessing the uncertainty in multivariate regression coefficients, namely, an approximate variance expression and four resampling methods (jack-knife, bootstrapping objects, bootstrapping residuals, and noise addition). The comparison is carried out for simulated as well as real near-infrared data. The calibration methods considered are ordinary least squares (simulated data), partial least squares regression, and principal component regression (real data). The results suggest that the approximate variance expression is a viable alternative to resampling.