Understanding the factors that may impact how well universities are transforminga set of inputs into research outputs is of great interest for university andpublic authorities. The goal of this paper is on the one hand to measure the researchefficiencies of US universities and on the other hand...

In this paper, we follow the same logic as in Hausman (1978) to create a testing procedure that checks for the presence of outliers by comparing a regression estimator that is robust to outliers (S-estimator), with another that is more e¢ cient but a¤ected by them. Some simulations are...

By using data collected through a survey containing newly enrolled student in Management Engineering at the University of Brussels, we show that even if all students do not come with the same chance of success at university, their working/studying behavior may lessen the burden of the past....

In this paper we study the factors that influence both dropout and degree completion (4 or 5 years to earn a degree) at university using survival analysis. In particular, we apply the set of discrete-time methods for competing risks event history analysis described in Scott and Kennedy (2005)....

Before implementing any multivariate statistical analysis based on em- pirical covariance matrices, it is important to check whether outliers are present because their existence could induce significant biases. In this article, we present the minimum covariance determinant estimator, which is...

In this paper we maximize the efficiency of a multivariate S-estimator under a constraint on the breakdown point. In the linear regression model, it is known that the highest possible efficiency of a maximum breakdown S-estimator is bounded above by 33% for Gaussian errors. We prove the...

The Sign Covariance Matrix is an orthogonal equivariant estimator of multivariate scale. It is often used as an easy-to-compute and highly robust estimator. In this paper we propose a k-step version of the Sign Covariance Matrix, which improves its efficiency while keeping the maximal breakdown...