Abstract The aim of this work is to compare EU countries in their efforts implement the circular economy model and indicate EU’s strategic objectives area, by analyzing indicators within member states. To achieve this, a qualitative quantitative analysis following THE EUROSTAT database has been carried out: total waste recycling rate, rate construction demolition waste, electronic contribution ...

This article considers algorithmic and statistical aspects of linear regression when the correspondence between the covariates and the responses is unknown. First, a fully polynomial-time approximation scheme is given for the natural least squares optimization problem in any constant dimension. Next, in an average-case and noise-free setting where the responses exactly correspond to a linear fu...

We consider the online sparse linear regression problem, which is the problem of sequentially making predictions observing only a limited number of features in each round, to minimize regret with respect to the best sparse linear regressor, where prediction accuracy is measured by square loss. We give an inefficient algorithm that obtains regret bounded by Õ( √ T ) after T prediction rounds. We...

When predicting scalar responses in the situation where the explanatory variables are functions, it is sometimes the case that some functional variables are related to responses linearly while other variables have more complicated relationships with the responses. In this paper, we propose a new semi-parametric model to take advantage of both parametric and nonparametric functional modeling. As...

In the Sparse Linear Regression (SLR) problem, given a d×n matrix M and a d-dimensional vector q, we want to compute a k-sparse vector τ such that the error ‖Mτ − q‖ is minimized. In this paper, we present algorithms and conditional lower bounds for several variants of this problem. In particular, we consider (i) the Affine SLR where we add the constraint that ∑ i τi = 1 and (ii) the Convex SLR...

We show that the definition of the θth sample quantile as the solution to a minimization problem introduced by Koenker and Basset [1978] can be easily extended to obtain an analogous definition for the θth sample quantity quantile instead of the usual one. By means of this definition we introduce a linear regression model for quantity quantiles and analyze some properties of the residuals. In s...

Theoretical results in the functional linear regression literature have so far focused on minimax estimation where smoothness parameters are assumed to be known and the estimators typically depend on these smoothness parameters. In this paper we consider adaptive estimation in functional linear regression. The goal is to construct a single data-driven procedure that achieves optimality results ...