Christophe Giraud – författare

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 AssociationIntroduction 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.

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.

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.

This edited volume traverses the spectrum of experiences that take place after children leave the family home and parents find themselves in the "empty nest" stage of life. Rather than focusing on measuring the intensity of empty nest syndrome or asking whether parents' marital satisfaction increases or decreases in this phase, the authors present rich qualitative data from across Poland and France to show that there is great variation in how families experience the empty nest, developing both a study on intimacy and love and on family solidarity. Throughout the book, themes of mixed emotions, nuanced attitudes, contradictions, and dissonance are explored while shedding light on "supporting actors" of the empty nest transition, such as family pets and material objects.

This edited volume traverses the spectrum of experiences that take place after children leave the family home and parents find themselves in the "empty nest" stage of life. Rather than focusing on measuring the intensity of empty nest syndrome or asking whether parents'' marital satisfaction increases or decreases in this phase, the authors present rich qualitative data from across Poland and France to show that there is great variation in how families experience the empty nest, developing both a study on intimacy and love and on family solidarity. Throughout the book, themes of mixed emotions, nuanced attitudes, contradictions, and dissonance are explored while shedding light on "supporting actors" of the empty nest transition, such as family pets and material objects.