Introduction to High Dimensional Statistics

This book PDF is perfect for those who love Business & Economics genre, written by Christophe Giraud and published by CRC Press which was released on 17 December 2014 with total hardcover pages 270. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Introduction to High Dimensional Statistics books below.

Introduction to High Dimensional Statistics
Author : Christophe Giraud
File Size : 42,9 Mb
Publisher : CRC Press
Language : English
Release Date : 17 December 2014
ISBN : 9781482237955
Pages : 270 pages
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Introduction to High Dimensional Statistics by Christophe Giraud Book PDF Summary

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

Introduction to High Dimensional Statistics

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

Get Book
High Dimensional Statistics

A coherent introductory text from a groundbreaking researcher, focusing on clarity and motivation to build intuition and understanding.

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Introduction to High Dimensional Statistics

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

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High Dimensional Probability

An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.

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Fundamentals of High Dimensional Statistics

This textbook provides a step-by-step introduction to the tools and principles of high-dimensional statistics. Each chapter is complemented by numerous exercises, many of them with detailed solutions, and computer labs in R that convey valuable practical insights. The book covers the theory and practice of high-dimensional linear regression, graphical models,

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Statistics for High Dimensional Data

Modern statistics deals with large and complex data sets, and consequently with models containing a large number of parameters. This book presents a detailed account of recently developed approaches, including the Lasso and versions of it for various models, boosting methods, undirected graphical modeling, and procedures controlling false positive selections.

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High Dimensional Data Analysis with Low Dimensional Models

Connects fundamental mathematical theory with real-world problems, through efficient and scalable optimization algorithms.

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High Dimensional Covariance Matrix Estimation

This book presents covariance matrix estimation and related aspects of random matrix theory. It focuses on the sample covariance matrix estimator and provides a holistic description of its properties under two asymptotic regimes: the traditional one, and the high-dimensional regime that better fits the big data context. It draws attention

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