Junwei Lu: Big Data Analysis, Gebunden
Big Data Analysis
- High Dimensional Probability, Statistics, Optimization, and Inference
- Publisher:
- Springer, 11/2025
- Binding:
- Gebunden
- Language:
- Englisch
- ISBN-13:
- 9783032031600
- Item number:
- 12361226
- Volume:
- 184 Pages
- Weight:
- 446 g
- Format:
- 241 x 160 mm
- Thickness:
- 16 mm
- Release date:
- 7.11.2025
- Note
-
Caution: Product is not in German language
Blurb
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.