We introduce and compare several robust procedures for bandwidth selection when estimating the variance function. These bandwidth selectors are to be used in combination with the robust scale estimates introduced by ...

We propose to smooth the entire objective function rather than only the check function in a linear quantile regression context. We derive a uniform Bahadur-Kiefer representation for the resulting convolution-type kernel ...

We propose to smooth the entire objective function rather than only the check function in a linear quantile regression context. We derive a uniform Bahadur-Kiefer representation for the resulting convolution-type kernel ...

Linearized inversion methods such as Gauss-Newton and multiple re-weighted leastsquares are iterative processes in which an update in the current model is computed as a function of data misfit and the gradient of data with ...

Nowadays, the task of predicting in schemas online is an essential field of study for machine learning. The Filters Adaptive based on kernel methods have taken an essential role in this type of task; this is primarily due ...

With the widespread use of commercial wireless communication networks such as Wi-Fi and Bluetooth, coupled with the increasing demand for new communications, especially driven by the concepts of the Internet of Things ...

This Ph.D. thesis explores stochastic optimization from a Distributionally Robust perspective, focusing on two significant themes: the innovative use of decision variable-dependent ambiguity sets in Distributionally Robust ...