We study the problem of estimating some unknown regression or autoregression function in a B-mixing dependent framework (for the design or the errors). For this end, we consider some collection of models which are finite dimensional spaces. A penalized least-squares estimator (PLSE) is built on...

In this paper the authors study the problem of non parametric estimation of an unknown regression function from dependent data with sub-Gaussian errors. As a particular case, they handle the autoregressive framework.

This paper studies a classical extension of the Black and Scholes model for option pricing, often known as the Hull and White model. Our specificity is that the volatility process is assumed not only to be stochastic, but also to have long memory features and properties.