We prove a Large Deviation Principle for the random spec- tral measure associated to the pair $(H_N; e)$ where $H_N$ is sampled in the GUE(N) and e is a fixed unit vector (and more generally in the $\beta$- extension of this model). The rate function consists of two parts. The contribution of the absolutely continuous part of the measure is the reversed Kullback information with respect to the semicircle distribution and the contribution of the singular part is connected to the rate function of the extreme eigenvalue in the GUE. This method is also applied to the Laguerre and Jacobi ensembles, but in thoses cases the expression of the rate function is not so explicit
Extended version with further details, comments and updated references.International audienceThis wo...
Extended version with further details, comments and updated references.International audienceThis wo...
Extended version with further details, comments and updated references.International audienceThis wo...
We prove a Large Deviation Principle for the random spec- tral measure associated to the pair $(H_N;...
We prove a Large Deviation Principle for the random spec- tral measure associated to the pair $(H_N;...
We prove a Large Deviation Principle for the random spec- tral measure associated to the pair $(H_N;...
International audienceWe continue to explore the connections between large deviations for objects co...
We continue to explore the connections between large deviations for objects coming from random matri...
We continue to explore the connections between large deviations for objects coming from random matri...
We continue to explore the connections between large deviations for objects coming from random matri...
We continue to explore the connections between large deviations for objects coming from random matri...
International audienceWe continue to explore the connections between large deviations for objects co...
This work is a companion paper of Gamboa, Nagel, Rouault (J. Funct. Anal. 2016). We continue to expl...
Extended version with further details, comments and updated references.International audienceThis wo...
Extended version with further details, comments and updated references.International audienceThis wo...
Extended version with further details, comments and updated references.International audienceThis wo...
Extended version with further details, comments and updated references.International audienceThis wo...
Extended version with further details, comments and updated references.International audienceThis wo...
We prove a Large Deviation Principle for the random spec- tral measure associated to the pair $(H_N;...
We prove a Large Deviation Principle for the random spec- tral measure associated to the pair $(H_N;...
We prove a Large Deviation Principle for the random spec- tral measure associated to the pair $(H_N;...
International audienceWe continue to explore the connections between large deviations for objects co...
We continue to explore the connections between large deviations for objects coming from random matri...
We continue to explore the connections between large deviations for objects coming from random matri...
We continue to explore the connections between large deviations for objects coming from random matri...
We continue to explore the connections between large deviations for objects coming from random matri...
International audienceWe continue to explore the connections between large deviations for objects co...
This work is a companion paper of Gamboa, Nagel, Rouault (J. Funct. Anal. 2016). We continue to expl...
Extended version with further details, comments and updated references.International audienceThis wo...
Extended version with further details, comments and updated references.International audienceThis wo...
Extended version with further details, comments and updated references.International audienceThis wo...
Extended version with further details, comments and updated references.International audienceThis wo...
Extended version with further details, comments and updated references.International audienceThis wo...