Earlier researchers have studied some aspects of the classes of distribution functions with decreasing ?-percentile residual life (DPRL(?)), 0 ? 1. The purpose of this paper is to note some further properties of these classes, and to initiate a theory of nonparametric statistical estimation of...

In this paper we study a family of stochastic orders of random variables defined via the comparison of their percentile residual life functions. Some interpretations of these stochastic orders are given, and various properties of them are derived. The relationships to other stochastic orders are...

The comparison of the means of two independent samples is one of the most popular problems in real-world data analysis. In the multivariate context, two-sample Hotelling's T² frequently used to test the equality of means of two independent Gaussian random samples assuming either the same or a...

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 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...

This paper proposes methods to detect outliers in functional datasets. We are interested in challenging scenarios where functional samples are contaminated by outliers that may be difficult to recognize. The task of identifying a typical curves is carried out using the recently proposed...

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...

This paper presents a general notion of Mahalanobis distance for functional data that extends the classical multivariate concept to situations where the observed data are points belonging to curves generated by a stochastic process. More precisely, a new semi-distance for functional observations...