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Big data analysis = high dimensional probability, statistics, optimization, and inference /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Big data analysis/ by Junwei Lu.
Reminder of title:	high dimensional probability, statistics, optimization, and inference /
Author:	Lu, Junwei.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xiii, 170 p. :ill., digital ;24 cm.
[NT 15003449]:	Part I Foundations of Big Data Analysis -- Chapter 1 Introduction -- Chapter 2 Preliminaries in Probability -- Chapter 3 Preliminaries in Linear Algebra -- Part II High-Dimensional Probability -- Chapter 4 Concentration Inequalities -- Chapter 5 Sub-Exponential Random Variables -- Chapter 6 Maximal Inequality -- Part III High-Dimensional Statistics -- Chapter 7 Ordinary Least Squares -- Chapter 8 Compressive Sensing -- Chapter 9 Restricted Isometry Property -- Chapter 10 Statistical Properties of Lasso -- Chapter 11 Variations of Lasso -- Part IV High-Dimensional Optimization -- Chapter 12 Convexity and Subgradient -- Chapter 13 Gradient Descent -- Chapter 14 Proximal Gradient Descent -- Chapter 15 Mirror Descent and Nesterov's Smoothing -- Chapter 16 Duality and ADMM -- Part V High-Dimensional Inference -- Chapter 17 High Dimensional Inference -- Chapter 18 Debiased Lasso -- Chapter 19 Multiple Hypotheses -- Chapter 20 False Discovery Rate -- Chapter 21 Knock-Off -- References.
Contained By:	Springer Nature eBook
Subject:	Statistics. -
Online resource:	https://doi.org/10.1007/978-3-032-03161-7
ISBN:	9783032031617

Big data analysis = high dimensional probability, statistics, optimization, and inference /
Lu, Junwei.

Big data analysishigh dimensional probability, statistics, optimization, and inference /[electronic resource] :by Junwei Lu. - Cham :Springer Nature Switzerland :2025. - xiii, 170 p. :ill., digital ;24 cm.

Part I Foundations of Big Data Analysis -- Chapter 1 Introduction -- Chapter 2 Preliminaries in Probability -- Chapter 3 Preliminaries in Linear Algebra -- Part II High-Dimensional Probability -- Chapter 4 Concentration Inequalities -- Chapter 5 Sub-Exponential Random Variables -- Chapter 6 Maximal Inequality -- Part III High-Dimensional Statistics -- Chapter 7 Ordinary Least Squares -- Chapter 8 Compressive Sensing -- Chapter 9 Restricted Isometry Property -- Chapter 10 Statistical Properties of Lasso -- Chapter 11 Variations of Lasso -- Part IV High-Dimensional Optimization -- Chapter 12 Convexity and Subgradient -- Chapter 13 Gradient Descent -- Chapter 14 Proximal Gradient Descent -- Chapter 15 Mirror Descent and Nesterov's Smoothing -- Chapter 16 Duality and ADMM -- Part V High-Dimensional Inference -- Chapter 17 High Dimensional Inference -- Chapter 18 Debiased Lasso -- Chapter 19 Multiple Hypotheses -- Chapter 20 False Discovery Rate -- Chapter 21 Knock-Off -- References.

This book covers the methods and theory of high dimensional probability, statistics, large-scale optimization, and inference. We aim to quickly bring readers to the frontier and interdisciplinary areas of statistics, optimization, probability, and machine learning. This book covers topics in: High dimensional probability, Concentration inequality, Sub-Gaussian random variables, Chernoff bounds, Hoeffding's inequality, Maximal inequalities, High dimensional linear regression, Ordinary least square, Compressed sensing, Lasso, Variations of Lasso including group lasso, fused lasso, adaptive lasso, etc., General high dimensional M- estimators, Variable selection consistency, High dimensional Optimization, Convex geometry, Lagrange duality, Gradient descent, Proximal gradient descent, LARS, ADMM, Mirror descent, Stochastic optimization, Large-Scale Inference, Linear model hypothesis testing, high dimensional inference, Chi-square test, maximal test, and Higher criticism, False discovery rate control.

ISBN: 9783032031617

Standard No.: 10.1007/978-3-032-03161-7doiSubjects--Topical Terms:

517247
Statistics.

LC Class. No.: QA276

Dewey Class. No.: 519.5

Big data analysis = high dimensional probability, statistics, optimization, and inference /
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505 0 $a Part I Foundations of Big Data Analysis -- Chapter 1 Introduction -- Chapter 2 Preliminaries in Probability -- Chapter 3 Preliminaries in Linear Algebra -- Part II High-Dimensional Probability -- Chapter 4 Concentration Inequalities -- Chapter 5 Sub-Exponential Random Variables -- Chapter 6 Maximal Inequality -- Part III High-Dimensional Statistics -- Chapter 7 Ordinary Least Squares -- Chapter 8 Compressive Sensing -- Chapter 9 Restricted Isometry Property -- Chapter 10 Statistical Properties of Lasso -- Chapter 11 Variations of Lasso -- Part IV High-Dimensional Optimization -- Chapter 12 Convexity and Subgradient -- Chapter 13 Gradient Descent -- Chapter 14 Proximal Gradient Descent -- Chapter 15 Mirror Descent and Nesterov's Smoothing -- Chapter 16 Duality and ADMM -- Part V High-Dimensional Inference -- Chapter 17 High Dimensional Inference -- Chapter 18 Debiased Lasso -- Chapter 19 Multiple Hypotheses -- Chapter 20 False Discovery Rate -- Chapter 21 Knock-Off -- References.
520 $a This book covers the methods and theory of high dimensional probability, statistics, large-scale optimization, and inference. We aim to quickly bring readers to the frontier and interdisciplinary areas of statistics, optimization, probability, and machine learning. This book covers topics in: High dimensional probability, Concentration inequality, Sub-Gaussian random variables, Chernoff bounds, Hoeffding's inequality, Maximal inequalities, High dimensional linear regression, Ordinary least square, Compressed sensing, Lasso, Variations of Lasso including group lasso, fused lasso, adaptive lasso, etc., General high dimensional M- estimators, Variable selection consistency, High dimensional Optimization, Convex geometry, Lagrange duality, Gradient descent, Proximal gradient descent, LARS, ADMM, Mirror descent, Stochastic optimization, Large-Scale Inference, Linear model hypothesis testing, high dimensional inference, Chi-square test, maximal test, and Higher criticism, False discovery rate control.
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