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Ever-greater computing technologies have given rise to an exponentially growing volume of data. Today massive data sets (with potentially thousands of variables) play an important role in almost every branch of modern human activity, including networks, finance, and genetics. However, analyzing such data has presented a challenge for statisticians and data analysts and has required the development of new statistical methods capable of separating the signal from the noise. Introduction to High-Dimensional Statistics is a concise guide to state-of-the-art models, techniques, and approaches for handling high-dimensional data. The book is intended to expose the reader to the key concepts and ideas in the most simple settings possible while avoiding unnecessary technicalities. Offering a succinct presentation of the mathematical foundations of high-dimensional statistics, this highly accessible text: Describes the challenges related to the analysis of high-dimensional data Covers cutting-edge statistical methods including model selection, sparsity and the lasso, aggregation, and learning theory Provides detailed exercises at the end of every chapter with collaborative solutions on a wikisite Illustrates concepts with simple but clear practical examples Introduction to High-Dimensional Statistics is suitable for graduate students and researchers interested in discovering modern statistics for massive data. It can be used as a graduate text or for self-study.

Introduction to High-Dimensional Statistics (Chapman & Hall/CRC Monographs on Statistics and Applied Probability) PDF ePub

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IntroductiontoHigh-DimensionalStatistics ~ Statistics for High-Dimensional Data by Peter Bu¨hlmann and Sara van de Geer (2001), which is widely considered as the main reference on the theory of high-dimensional statistics. These years have allowed Giraud to distill the core elements of the literature and simplify some of the arguments so that Introduction to High-Dimensional Statistics can

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High Dimensional Statistics - MIT Mathematics ~ high-dimensional statistics would not have been possible without standing on the shoulders of giants. In these notes, we will explore one, obviously subjec-tive giant on whose shoulders high-dimensional statistics stand: nonparametric statistics. The works of Ibragimov and Has’minskii in the seventies followed by many

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High-dimensional statistics / ENSAE Paris ~ C.Giraud. Introduction to high-dimensional statistics. Chapman and Hall, 2015. A.B.Tsybakov. Apprentissage statistique et estimation non-paramétrique. Polycopié de l'Ecole Polytechnique, 2014. S.van de Geer. Estimation and testing under sparsity. Lecture Notes in Mathematics 2159. Springer, 2016.

Introduction To High Dimensional Statistics Chapman Hall ~ Introduction To High Dimensional Statistics Chapman Hall Crc Monographs On Statistics Applied Probability Author: wiki.ctsnet-Matthias Durr-2020-09-03-00-56-39 Subject: Introduction To High Dimensional Statistics Chapman Hall Crc Monographs On Statistics Applied Probability Keywords

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High-dimensional statistics - Wikipedia ~ In statistical theory, the field of high-dimensional statistics studies data whose dimension is larger than dimensions considered in classical multivariate analysis.High-dimensional statistics relies on the theory of random vectors.In many applications, the dimension of the data vectors may be larger than the sample size.

Introduction to High-Dimensional Statistics (Chapman ~ Introduction to High-Dimensional Statistics is a concise guide to state-of-the-art models, techniques, and approaches for handling high-dimensional data. The book is intended to expose the reader to the key concepts and ideas in the most simple settings possible while avoiding unnecessary technicalities.

Introduction to High-Dimensional Statistics (Chapman ~ Introduction to High-Dimensional Statistics (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) by Christophe Giraud (2015-01-07): Christophe Giraud;: 0783324902976: Books - .ca

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