The field of nonparametric function estimation has broadened its appeal in recent years with
an array of new tools for statistical analysis. In particular, theoretical and applied research on …

Recent work in machine learning shows that deep neural networks can be used to solve a
wide variety of inverse problems arising in computational imaging. We explore the central …

Orthogonal statistical learning Page 1 The Annals of Statistics 2023, Vol. 51, No. 3, 879–908
https://doi.org/10.1214/23-AOS2258 © Institute of Mathematical Statistics, 2023 ORTHOGONAL …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

Many challenging image processing tasks can be described by an ill-posed linear inverse
problem: deblurring, deconvolution, inpainting, compressed sensing, and superresolution all …

The word 'multiscale'can mean many things. However, in this book we are generally
concerned with the representation of objects at a set of scales and then manipulating these …

This paper explores a class of empirical Bayes methods for level-dependent threshold
selection in wavelet shrinkage. The prior considered for each wavelet coefficient is a mixture …

Wavelet analysis has been found to be a powerful tool for the nonparametric estimation of
spatially-variable objects. We discuss in detail wavelet methods in nonparametric …

We derive minimax rates for estimation in anisotropic smoothness classes. These rates are
attained by a coordinatewise thresholded wavelet estimator based on a tensor product basis …

Wavelet Analysis: Basic Concepts and Applications provides a basic and self-contained
introduction to the ideas underpinning wavelet theory and its diverse applications. This book …