Aimed at Masters or PhD level students in statistics, computer science,
and engineering, this comprehensive text provides the reader with a
single book where they can find accounts of a number of up-to-date
issues in nonparametric inference, all set out with exceptional clarity.
It is also suitable for researchers who want to get up to speed quickly
on modern nonparametric methods. With an exhaustive exploration of
asymptotic nonparametric inferences, it also covers a huge range of
other crucial topic areas including the bootstrap, the nonparametric
delta method, nonparametric regression, density estimation, orthogonal
function methods, minimax estimation, nonparametric confidence sets, and
wavelets. The book's dual approach includes a mixture of methodology and
theory.