Introduction to High-Dimensional Statistics (2nd Ed.) Chapman & Hall/CRC Monographs on Statistics and Applied Probability Series
Auteur : Giraud Christophe
Praise for the first edition:
"[This book] succeeds singularly at providing a structured introduction to this active field of research. ? it is arguably the most accessible overview yet published of the mathematical ideas and principles that one needs to master to enter the field of high-dimensional statistics. ? recommended to anyone interested in the main results of current research in high-dimensional statistics as well as anyone interested in acquiring the core mathematical skills to enter this area of research."
?Journal of the American Statistical Association
Introduction to High-Dimensional Statistics, Second Edition preserves the philosophy of the first edition: to be a concise guide for students and researchers discovering the area and interested in the mathematics involved. The main concepts and ideas are presented in simple settings, avoiding thereby unessential technicalities. High-dimensional statistics is a fast-evolving field, and much progress has been made on a large variety of topics, providing new insights and methods. Offering a succinct presentation of the mathematical foundations of high-dimensional statistics, this new edition:
- Offers revised chapters from the previous edition, with the inclusion of many additional materials on some important topics, including compress sensing, estimation with convex constraints, the slope estimator, simultaneously low-rank and row-sparse linear regression, or aggregation of a continuous set of estimators.
- Introduces three new chapters on iterative algorithms, clustering, and minimax lower bounds.
- Provides enhanced appendices, minimax lower-bounds mainly with the addition of the Davis-Kahan perturbation bound and of two simple versions of the Hanson-Wright concentration inequality.
- Covers cutting-edge statistical methods including model selection, sparsity and the Lasso, iterative hard thresholding, aggregation, support vector machines, and learning theory.
- Provides detailed exercises at the end of every chapter with collaborative solutions on a wiki site.
- Illustrates concepts with simple but clear practical examples.
Christophe Giraud was a student of the École Normale Supérieure de Paris, and he received a Ph.D in probability theory from the University Paris 6. He was assistant professor at the University of Nice from 2002 to 2008. He has been associate professor at the École Polytechnique since 2008 and professor at Paris Sud University (Orsay) since 2012. His current research focuses mainly on the statistical theory of high-dimensional data analysis and its applications to life sciences.
Date de parution : 08-2021
15.6x23.4 cm
Thèmes d’Introduction to High-Dimensional Statistics :
Mots-clés :
Lasso Estimator; High Dimensional Settings; sparse regression; Oracle Inequalities; low-rank regression; Benjamini Hochberg Procedure; lasso-like estimators; Oracle Risk; aggregation; Empirical Covariance Matrix; estimator selection; Convex Criterion; graphical models; Model Selection; FDR control; Symmetric Positive Definite Matrices; support vector machine; Moore Penrose Pseudo-inverse; classification; Sparse Additive Model; Data Processing Inequality; penalized criteria; Sparse Group Lasso; high-dimensional data; Minimax Risk; Metropolis Hastings Algorithm; convex minimization; Misclassification Probability; concentration inequalities; reproducing kernel Hilbert spaces; Bayes Classifier; Ridge Estimator; Precision Matrix; Lar Algorithm; VC Dimension; Positive Continuous Density; Adaptive Lasso Estimator; KPCA