We show that Cohen’s Kappa and Matthews Correlation Coefficient (MCC), both extended and contrasted measures of performance in multi-class classification, are correlated in most situations, albeit can differ in others. Indeed, although in the symmetric case both match, we consider different unbalanced situations in which Kappa exhibits an undesired behaviour, i.e. a worse classifier gets higher Kappa score, differing qualitatively from that of MCC. The debate about the incoherence in the behaviour of Kappa revolves around the convenience, or not, of using a relative metric, which makes the interpretation of its values difficult. We extend these concerns by showing that its pitfalls can go even further. Through experimentation, we present a novel approach to this topic. We carry on a comprehensive study that identifies an scenario in which the contradictory behaviour among MCC and Kappa emerges. Specifically, we find out that when there is a decrease to zero of the entropy of the elements out of the diagonal of the confusion matrix associated to a classifier, the discrepancy between Kappa and MCC rise, pointing to an anomalous performance of the former. We believe that this finding disables Kappa to be used in general as a performance measure to compare classifiers.

Calibration curves allow instrument calibration by predicting the concentration of an analyte in a sample from the reading of the instrument. This curve is constructed as the regression straight line that best fits the relationship between some known concentration standards and their respective instrument readings. An example is the Beer–Lambert Law, used to predict the concentration of a new sample from its absorbance obtained by spectrometry. The issue is that usually this methodology is misapplied. In this paper, we want to clarify this point, explaining what the error consists of and how (easily) to fix it, with the intention of ensuring that it does not continue to be reproduced in the experimental scientific work.

We prove that, under rather general conditions, the law of a continuous Gaussian process represented by a stochastic integral of a deterministic kernel, with respect to a standard Wiener process, can be weakly approximated by the law of some processes constructed from a standard Poisson process. An example of a Gaussian process to which this result applies is the fractional Brownian motion with any Hurst parameter.

This survey paper is devoted to stability analysis of regenerative queueing systems. The method is based on the renewal technique and allows to obtain simple proofs of the sufficient stability conditions which are close to being necessary. The applicability of the method is illustrated by a number of well-known and also less known queueing systems. An extension of the method to the systems under arbitrary initial states is given.