Fundamentals of High Dimensional Statistics

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Read or download book entitled Fundamentals of High Dimensional Statistics written by Johannes Lederer and published by Springer Nature in PDF, EPUB and Kindle Format. Click Get This Book button to download or read online books. Join over 650.000 happy Readers and READ as many books as you like. We cannot guarantee that Fundamentals of High Dimensional Statistics book is available in the library.

  • Publisher : Springer Nature
  • Release : 16 November 2021
  • ISBN : 9783030737924
  • Page : 355 pages
  • Rating : 4.5/5 from 103 voters

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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, and inference, ensuring readers have a smooth start in the field. It also offers suggestions for further reading. Given its scope, the textbook is intended for beginning graduate and advanced undergraduate students in statistics, biostatistics, and bioinformatics, though it will be equally useful to a broader audience.

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Fundamentals of High-Dimensional Statistics

Fundamentals of High-Dimensional Statistics
  • Author : Johannes Lederer
  • Publisher : Springer Nature
  • Release Date : 2021-11-16
  • ISBN : 9783030737924
GET THIS BOOKFundamentals 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, and inference, ensuring readers have a smooth start in the field. It also offers suggestions for further reading. Given its scope, the textbook is intended for beginning graduate and advanced

Introduction to High-Dimensional Statistics

Introduction to High-Dimensional Statistics
  • Author : Christophe Giraud
  • Publisher : CRC Press
  • Release Date : 2021-08-25
  • ISBN : 9781000408324
GET THIS BOOKIntroduction 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 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

Statistics for High-Dimensional Data

Statistics for High-Dimensional Data
  • Author : Peter Bühlmann,Sara van de Geer
  • Publisher : Springer Science & Business Media
  • Release Date : 2011-06-08
  • ISBN : 9783642201929
GET THIS BOOKStatistics 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. A special characteristic of the book is that it contains comprehensive mathematical theory on high-dimensional statistics combined with methodology, algorithms and illustrations with real data examples. This in-depth approach highlights

Foundations of Data Science

Foundations of Data Science
  • Author : Avrim Blum,John Hopcroft,Ravi Kannan
  • Publisher : Cambridge University Press
  • Release Date : 2020-01-31
  • ISBN : 9781108485067
GET THIS BOOKFoundations of Data Science

Covers mathematical and algorithmic foundations of data science: machine learning, high-dimensional geometry, and analysis of large networks.

High-Dimensional Data Analysis with Low-Dimensional Models

High-Dimensional Data Analysis with Low-Dimensional Models
  • Author : John Wright,Yi Ma
  • Publisher : Cambridge University Press
  • Release Date : 2021-12-31
  • ISBN : 9781108489737
GET THIS BOOKHigh-Dimensional Data Analysis with Low-Dimensional Models

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

High-Dimensional Probability

High-Dimensional Probability
  • Author : Roman Vershynin
  • Publisher : Cambridge University Press
  • Release Date : 2018-09-27
  • ISBN : 9781108415194
GET THIS BOOKHigh-Dimensional Probability

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

High-Dimensional Statistics

High-Dimensional Statistics
  • Author : Martin J. Wainwright
  • Publisher : Cambridge University Press
  • Release Date : 2019-02-21
  • ISBN : 9781108498029
GET THIS BOOKHigh-Dimensional Statistics

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

Statistical Foundations of Data Science

Statistical Foundations of Data Science
  • Author : Jianqing Fan,Runze Li,Cun-Hui Zhang,Hui Zou
  • Publisher : CRC Press
  • Release Date : 2020-09-21
  • ISBN : 9781466510852
GET THIS BOOKStatistical Foundations of Data Science

Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data

Mathematical Foundations of Infinite-Dimensional Statistical Models

Mathematical Foundations of Infinite-Dimensional Statistical Models
  • Author : Anonim
  • Publisher : Unknown
  • Release Date : 2022-05-29
  • ISBN : 9781107043169
GET THIS BOOKMathematical Foundations of Infinite-Dimensional Statistical Models

An Introduction to Statistical Learning

An Introduction to Statistical Learning
  • Author : Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani
  • Publisher : Springer Science & Business Media
  • Release Date : 2013-06-24
  • ISBN : 9781461471387
GET THIS BOOKAn Introduction to Statistical Learning

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more.

Fundamentals of Nonparametric Bayesian Inference

Fundamentals of Nonparametric Bayesian Inference
  • Author : Subhashis Ghosal,Aad van der Vaart
  • Publisher : Cambridge University Press
  • Release Date : 2017-06-26
  • ISBN : 9780521878265
GET THIS BOOKFundamentals of Nonparametric Bayesian Inference

Bayesian nonparametrics comes of age with this landmark text synthesizing theory, methodology and computation.

High-dimensional Statistics

High-dimensional Statistics
  • Author : Johannes Lederer
  • Publisher : Unknown
  • Release Date : 2020
  • ISBN : 1119536901
GET THIS BOOKHigh-dimensional Statistics

"An Introduction to Regularized Estimation in High Dimensions considers statistical theory, methods, and algorithms for large and complex data. The main focus is on regularized estimators, which are at the cusp of entering the statistical toolkits of almost all scientific disciplines. This book provides clear expositions, motivational introductions to each chapter, rigorous step-by-step proofs, and comprehensive exercise sets with fully worked out solutions. These features make this book ideal for graduate level courses. Moreover, the book also discusses cutting-edge topics,

The R Software

The R Software
  • Author : Pierre Lafaye de Micheaux,Rémy Drouilhet,Benoit Liquet
  • Publisher : Springer Science & Business
  • Release Date : 2014-05-13
  • ISBN : 9781461490203
GET THIS BOOKThe R Software

The contents of The R Software are presented so as to be both comprehensive and easy for the reader to use. Besides its application as a self-learning text, this book can support lectures on R at any level from beginner to advanced. This book can serve as a textbook on R for beginners as well as more advanced users, working on Windows, MacOs or Linux OSes. The first part of the book deals with the heart of the R language

Mathematics for Machine Learning

Mathematics for Machine Learning
  • Author : Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong
  • Publisher : Cambridge University Press
  • Release Date : 2020-03-31
  • ISBN : 9781108470049
GET THIS BOOKMathematics for Machine Learning

Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.

Fundamentals of Database Indexing and Searching

Fundamentals of Database Indexing and Searching
  • Author : Arnab Bhattacharya
  • Publisher : CRC Press
  • Release Date : 2014-12-02
  • ISBN : 9781466582545
GET THIS BOOKFundamentals of Database Indexing and Searching

Fundamentals of Database Indexing and Searching presents well-known database searching and indexing techniques. It focuses on similarity search queries, showing how to use distance functions to measure the notion of dissimilarity. After defining database queries and similarity search queries, the book organizes the most common and representative index structures according to their characteristics. The author first describes low-dimensional index structures, memory-based index structures, and hierarchical disk-based index structures. He then outlines useful distance measures and index structures that use the