This article introduces the large portfolio selection using gross-exposure constraints. It
shows that with gross-exposure constraints, the empirically selected optimal portfolios based …

We model a large panel of time series as a vector autoregression where the autoregressive
matrices and the inverse covariance matrix of the system innovations are assumed to be …

In this paper, we perform deep neural networks for learning stationary ψ-weakly dependent
processes. Such weak-dependence property includes a class of weak dependence …

In this paper we obtain a Bernstein type inequality for a class of weakly dependent and
bounded random variables. The proofs lead to a moderate deviations principle for sums of …

Abstract The Sliced-Wasserstein distance (SW) is being increasingly used in machine
learning applications as an alternative to the Wasserstein distance and offers significant …

Recent developments on deep learning established some theoretical properties of deep
neural networks estimators. However, most of the existing works on this topic are restricted …

We propose a methodology for detecting multiple change points in the mean of an otherwise
stationary, autocorrelated, linear time series. It combines solution path generation based on …

Many methods for statistical inference and generative modeling rely on a probability
divergence to effectively compare two probability distributions. The Wasserstein distance …

The survey is dedicated to a celebrated series of quantitave results, developed by the
Lithuanian school of probability, on the normal approximation for a real-valued random …

Abstract We prove a Berry–Esseen theorem and Edgeworth expansions for partial sums of
the form SN=∑ n= 1 N fn (X n, X n+ 1), where {X n} is a uniformly elliptic inhomogeneous …