Learning the dependence structure of rare events: a non-asymptotic study

The experimental points give, for each compounds, the measured paramagnetic chemical shift ; the values of Np in abscissa are obtained as described in. the

Dimension is an inherent bottleneck to some modern learning tasks, where optimization methods suffer from the size of the data. In this paper, we study non-isotropic distributions

We extend the general local risk minimisation approach introduced in [1] to account for liquidity costs, and derive the corresponding optimal strategies in both the discrete-

First introduced in the seminal work of [4], the quadratic local risk minimization approach to derivative hedging is well understood, and its connection to pricing under the

Concerning the estimation in the ergodic case, a sequential procedure was proposed in [26] for nonparametric estimating the drift coefficient in an integral metric and in [22] a

Fourdrinier and Pergamenshchikov [5] extended the Barron-Birge-Massart method to the models with dependent observations and, in contrast to all above-mentioned papers on the

The first goal of the research is to construct an adaptive procedure for estimating the function S which does not use any smoothness information of S and which is based on

The first goal of the research is to construct an adap- tive procedure based on observations (y j ) 1≤j≤n for estimating the function S and to obtain a sharp non-asymptotic upper