In nonparametric and high-dimensional Statistical models, the classical Gauss-Fisher-Le Cam theory of the optimality of maximum likelihood estimators and Bayesian posterior inference does not apply, and new Foundations and ideas have been developed in the past several decades.
The Mathematical Foundations include self-contained \'mini-courses\' on the theory of Gaussian and empirical processes.
This book gives a coherent account of the Statistical theory in Infinite-Dimensional parameter spaces.
In nonparametric and high-dimensional Statistical models, the classical Gauss-Fisher-Le Cam theory of the optimality of maximum likelihood estimators and Bayesian posterior inference does not apply, and new Foundations and ideas have been developed in the past several decades
