Inverse vs. classical calibration for small data sets

In classical calibration, the statistically uncertain variable y is regressed on the error-free variable x for a number of known samples, and the results are used to estimate the x value (x0) for an unknown sample from its measured y value (y0). It has long been known that inverse calibration--regression of x on y for the same data--is more efficient in its prediction of x0 from y0 than the seemingly more appropriate classical procedure, over large ranges of the controlled variable x. In the present work, theoretical expressions and Monte Carlo calculations are used to illustrate that the comparison favors the inverse procedure even more for small calibration data sets than for the large sets that have been emphasized in previous studies.