Remote sensing data present peculiar features and characteristics that may make their statistical processing and analysis a difficult task. Among them, it can be mentioned the volume of data involved, the redundancy, the ...

When dealing with several populations of functional data, equality of the covariance operators is often assumed even when seeking for a lower-dimensional approximation to the data. Usually, if this assumption does not hold, ...

The purpose of this paper is to classify and characterize 64 banks, active as of 2010 inArgentina, by means of robust techniques used on information gathered during the period 2001-2010. Based on the strategy criteria ...

In the finite-dimensional setting, Li and Chen (1985) proposed a method for principal components analysis using projection-pursuit techniques. This procedure was generalized to the functional setting by Bali et al. (2011), ...

Principal component analysis is a widely used technique that provides an optimal lower-dimensional approximation to multivariate or functional datasets. These approximations can be very useful in identifying potential ...

Detecting outlying observations is an important step in any analysis, even when robust estimates are used. In particular, the robustified Mahalanobis distance is a natural measure of outlyingness if one focuses on ellipsoidal ...

This paper presents a network anomaly detection method based on fuzzy clustering. Computer security has become an increasingly vital field in computer science in response to the proliferation of private sensitive information. ...

In this paper, we analyse the recent principal volatility components analysis procedure. The procedure overcomes several diculties in modelling and forecasting the conditional covariance matrix in large dimensions arising ...

The current water scarcity faced by many countries increases the need to consider an appropriate representation of future hydro inflows in power system operation and planning models. Hydrothermal scheduling is the problem ...