Consistency factor for the MCD estimator at the Student-t distribution

Abstract

It is well known that trimmed estimators of multivariate scatter, such as the Minimum Covariance Determinant (MCD) estimator, are inconsistent unless an appropriate factor is applied to them in order to take the effect of trimming into account. This factor is widely recommended and applied when uncontaminated data are assumed to come from a multivariate normal model. We address the problem of computing a consistency factor for the MCD estimator in a heavy-tail scenario, when uncontaminated data come from a multivariate Student-t distribution. We derive a remarkably simple computational formula for the appropriate factor and show that it reduces to an even simpler analytic expression in the bivariate case. Exploiting our formula, we then develop a robust Monte Carlo procedure for estimating the usually unknown number of degrees of freedom of the assumed and possibly contaminated multivariate Student-t model, which is a necessary ingredient for obtaining the required consistency factor. Finally, we provide substantial simulation evidence about the proposed procedure and apply it to data from image processing and financial markets.

Communicating educational innovation projects in Latin America mediated by the scaling of complex thinking: Contribution of the UNESCO-ICDE Chair in Mexico

“…This result transcends mere general improvement, specifically showing in which aspects the participants' perceptions experienced notable transformations. The Student t test, supported by Pearson correlation and Tukey's method, not only quantifies the overall improvement in perception, but also contextualizes and specifies the specific areas, where changes have occurred (Barabesi et al, 2023). This analysis demonstrates the importance of the t-test in providing a complete and detailed understanding of the impact of the Bootcamp on the development of complex thinking competencies among UNESCO Chair participants.…”