Sparse space-time models: Concentration Inequalities and Lasso

07/19/2018
by   Guilherme Ost, et al.
0

Inspired by Kalikow-type decompositions, we introduce a new stochastic model of infinite neuronal networks, for which we establish oracle inequalities for Lasso methods and restricted eigenvalue properties for the associated Gram matrix with high probability. These results hold even if the network is only partially observed. The main argument rely on the fact that concentration inequalities can easily be derived whenever the transition probabilities of the underlying process admit a sparse space-time representation.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset