In this paper we introduce a new multivariate stochastic order that compares random vectors in a direction which is determined by a unit vector, generalizing previous upper and lower orthant order. The main properties of this new order, together with its relationships with other multivariate...

In economics, insurance and finance, value at risk (VaR) is a widely used measure of the risk of loss on a specific portfolio of financial assets. For a given portfolio, time horizon, and probability alfa, the 100alfa% VaR is defined as a threshold loss value, such that the probability that the...

In this paper, we introduce a new multivariate stochastic order that compares random vectors in a direction which is determined by a unit vector, generalizing the previous upper and lower orthant orders. The main properties of this new order, together with its relationships with other...

In economics, insurance and finance, value at risk (VaR) is a widely used measure of the risk of loss on a specific portfolio of financial assets. For a given portfolio, time horizon, and probability $\alpha$, the $100\alpha\%$ VaR is defined as a threshold loss value, such that the probability...

In the context of functional data analysis, we propose a new method to test the homogeneity of families of functions. Based on some well-known depth measures, we construct four different statistics in order to measure distance between the two families. A simulation study is performed to check...

The capability of modeling non-exponentially distributed and dependent inter-arrival times as well as correlated batches makes the Batch Markovian Arrival Processes (BMAP) suitable in different real-life settings as teletraffic, queueing theory or actuarial contexts. An issue to be taken into...

Measuring dependence is a basic question when dealing with functional observations. The usual correlation for curves is not robust. Kendall's coefficient is a natural description of dependence between finite dimensional random variables. We extend this concept to functional observations. Given a...

We present a notion of Spearman's coefficient for functional data that extends the classical bivariate concept to situations where the observed data are curves generated by a stochastic process. Since Spearman's coefficient for bivariate samples is based on the natural data ordering in dimension...

Functional Regression has been an active subject of research in the last two decades but still lacks a secure variable selection methodology. Lasso is a well known effective technique for parameters shrinkage and variable selection in regression problems. In this work we generalize the Lasso...