Information complexity of black-box convex optimization: a new look via feedback information theory
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Nonparametric estimation of the anisotropic probability density of mixed variables
Journal of Multivariate Analysis
F-transform with parametric generalized fuzzy partitions
Fuzzy Sets and Systems
The Journal of Machine Learning Research
Information Rates of Nonparametric Gaussian Process Methods
The Journal of Machine Learning Research
Union Support Recovery in Multi-task Learning
The Journal of Machine Learning Research
Patch reprojections for Non-Local methods
Signal Processing
Plug-in approach to active learning
The Journal of Machine Learning Research
Sparse regression learning by aggregation and Langevin Monte-Carlo
Journal of Computer and System Sciences
The Journal of Machine Learning Research
Dynamic Pricing Under a General Parametric Choice Model
Operations Research
Generalized Birnbaum-Saunders kernel density estimators and an analysis of financial data
Computational Statistics & Data Analysis
Minimax optimal estimation of general bandable covariance matrices
Journal of Multivariate Analysis
The Journal of Machine Learning Research
Derivative estimation with local polynomial fitting
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Tight lower bound for linear sketches of moments
ICALP'13 Proceedings of the 40th international conference on Automata, Languages, and Programming - Volume Part I
Persistence-Based Clustering in Riemannian Manifolds
Journal of the ACM (JACM)
A plug-in approach to neyman-pearson classification
The Journal of Machine Learning Research
Iterative bias reduction: a comparative study
Statistics and Computing
Approximation and Estimation Bounds for Subsets of Reproducing Kernel Kreĭn Spaces
Neural Processing Letters
Hi-index | 0.01 |
This is a concise text developed from lecture notes and ready to be used for a course on the graduate level. The main idea is to introduce the fundamental concepts of the theory while maintaining the exposition suitable for a first approach in the field. Therefore, the results are not always given in the most general form but rather under assumptions that lead to shorter or more elegant proofs. The book has three chapters. Chapter 1 presents basic nonparametric regression and density estimators and analyzes their properties. Chapter 2 is devoted to a detailed treatment of minimax lower bounds. Chapter 3 develops more advanced topics: Pinskers theorem, oracle inequalities, Stein shrinkage, and sharp minimax adaptivity. This book will be useful for researchers and grad students interested in theoretical aspects of smoothing techniques. Many important and useful results on optimal and adaptive estimation are provided. As one of the leading mathematical statisticians working in nonparametrics, the author is an authority on the subject.