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Handbook of Big Data

Author Peter Bühlmann , Petros Drineas , Michael Kane , Mark van der Laan
Release Date: 2016/02/01
ISBN: 9781482249088
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Handbook of Big Data provides a state-of-the-art overview of the analysis of large-scale datasets. Featuring contributions from well-known experts in statistics and computer science, this handbook presents a carefully curated collection of techniques from both industry and academia. Thus, the text instills a working understanding of key statistical

Table of Contents

  1. Front Cover (1/2)
  2. Front Cover (2/2)
  3. Contents
  4. Preface
  5. Editors
  6. Contributors
  7. I. General Perspectives on Big Data
    1. 1. The Advent of Data Science: Some Considerations on the Unreasonable Effectiveness of Data (1/4)
    2. 1. The Advent of Data Science: Some Considerations on the Unreasonable Effectiveness of Data (2/4)
    3. 1. The Advent of Data Science: Some Considerations on the Unreasonable Effectiveness of Data (3/4)
    4. 1. The Advent of Data Science: Some Considerations on the Unreasonable Effectiveness of Data (4/4)
    5. 2. Big-n versus Big-p in Big Data (1/3)
    6. 2. Big-n versus Big-p in Big Data (2/3)
    7. 2. Big-n versus Big-p in Big Data (3/3)
  8. II. Data-Centric, Exploratory Methods
    1. 3. Divide and Recombine: Approach for Detailed Analysis and Visualization of Large Complex Data (1/3)
    2. 3. Divide and Recombine: Approach for Detailed Analysis and Visualization of Large Complex Data (2/3)
    3. 3. Divide and Recombine: Approach for Detailed Analysis and Visualization of Large Complex Data (3/3)
    4. 4. Integrate Big Data for Better Operation, Control, and Protection of Power Systems (1/3)
    5. 4. Integrate Big Data for Better Operation, Control, and Protection of Power Systems (2/3)
    6. 4. Integrate Big Data for Better Operation, Control, and Protection of Power Systems (3/3)
    7. 5. Interactive Visual Analysis of Big Data (1/3)
    8. 5. Interactive Visual Analysis of Big Data (2/3)
    9. 5. Interactive Visual Analysis of Big Data (3/3)
    10. 6. A Visualization Tool for Mining Large Correlation Tables: The Association Navigator (1/6)
    11. 6. A Visualization Tool for Mining Large Correlation Tables: The Association Navigator (2/6)
    12. 6. A Visualization Tool for Mining Large Correlation Tables: The Association Navigator (3/6)
    13. 6. A Visualization Tool for Mining Large Correlation Tables: The Association Navigator (4/6)
    14. 6. A Visualization Tool for Mining Large Correlation Tables: The Association Navigator (5/6)
    15. 6. A Visualization Tool for Mining Large Correlation Tables: The Association Navigator (6/6)
  9. III. Efficient Algorithms
    1. 7. High-Dimensional Computational Geometry (1/4)
    2. 7. High-Dimensional Computational Geometry (2/4)
    3. 7. High-Dimensional Computational Geometry (3/4)
    4. 7. High-Dimensional Computational Geometry (4/4)
    5. 8. IRLBA: Fast Partial Singular Value Decomposition Method (1/3)
    6. 8. IRLBA: Fast Partial Singular Value Decomposition Method (2/3)
    7. 8. IRLBA: Fast Partial Singular Value Decomposition Method (3/3)
    8. 9. Structural Properties Underlying High-Quality Randomized Numerical Linear Algebra Algorithms (1/4)
    9. 9. Structural Properties Underlying High-Quality Randomized Numerical Linear Algebra Algorithms (2/4)
    10. 9. Structural Properties Underlying High-Quality Randomized Numerical Linear Algebra Algorithms (3/4)
    11. 9. Structural Properties Underlying High-Quality Randomized Numerical Linear Algebra Algorithms (4/4)
    12. 10. Something for (Almost) Nothing: New Advances in Sublinear-Time Algorithms (1/3)
    13. 10. Something for (Almost) Nothing: New Advances in Sublinear-Time Algorithms (2/3)
    14. 10. Something for (Almost) Nothing: New Advances in Sublinear-Time Algorithms (3/3)
  10. IV. Graph Approaches
    1. 11. Networks (1/4)
    2. 11. Networks (2/4)
    3. 11. Networks (3/4)
    4. 11. Networks (4/4)
    5. 12. Mining Large Graphs (1/6)
    6. 12. Mining Large Graphs (2/6)
    7. 12. Mining Large Graphs (3/6)
    8. 12. Mining Large Graphs (4/6)
    9. 12. Mining Large Graphs (5/6)
    10. 12. Mining Large Graphs (6/6)
  11. V. Model Fitting and Regularization
    1. 13. Estimator and Model Selection Using Cross-Validation (1/4)
    2. 13. Estimator and Model Selection Using Cross-Validation (2/4)
    3. 13. Estimator and Model Selection Using Cross-Validation (3/4)
    4. 13. Estimator and Model Selection Using Cross-Validation (4/4)
    5. 14. Stochastic Gradient Methods for Principled Estimation with Large Datasets (1/6)
    6. 14. Stochastic Gradient Methods for Principled Estimation with Large Datasets (2/6)
    7. 14. Stochastic Gradient Methods for Principled Estimation with Large Datasets (3/6)
    8. 14. Stochastic Gradient Methods for Principled Estimation with Large Datasets (4/6)
    9. 14. Stochastic Gradient Methods for Principled Estimation with Large Datasets (5/6)
    10. 14. Stochastic Gradient Methods for Principled Estimation with Large Datasets (6/6)
    11. 15. Learning Structured Distributions (1/4)
    12. 15. Learning Structured Distributions (2/4)
    13. 15. Learning Structured Distributions (3/4)
    14. 15. Learning Structured Distributions (4/4)
    15. 16. Penalized Estimation in Complex Models (1/4)
    16. 16. Penalized Estimation in Complex Models (2/4)
    17. 16. Penalized Estimation in Complex Models (3/4)
    18. 16. Penalized Estimation in Complex Models (4/4)
    19. 17. High-Dimensional Regression and Inference (1/4)
    20. 17. High-Dimensional Regression and Inference (2/4)
    21. 17. High-Dimensional Regression and Inference (3/4)
    22. 17. High-Dimensional Regression and Inference (4/4)
  12. VI. Ensemble Methods
    1. 18. Divide and Recombine: Subsemble, Exploiting the Power of Cross-Validation (1/4)
    2. 18. Divide and Recombine: Subsemble, Exploiting the Power of Cross-Validation (2/4)
    3. 18. Divide and Recombine: Subsemble, Exploiting the Power of Cross-Validation (3/4)
    4. 18. Divide and Recombine: Subsemble, Exploiting the Power of Cross-Validation (4/4)
    5. 19. Scalable Super Learning (1/4)
    6. 19. Scalable Super Learning (2/4)
    7. 19. Scalable Super Learning (3/4)
    8. 19. Scalable Super Learning (4/4)
  13. VII. Causal Inference
    1. 20. Tutorial for Causal Inference (1/6)
    2. 20. Tutorial for Causal Inference (2/6)
    3. 20. Tutorial for Causal Inference (3/6)
    4. 20. Tutorial for Causal Inference (4/6)
    5. 20. Tutorial for Causal Inference (5/6)
    6. 20. Tutorial for Causal Inference (6/6)
    7. 21. A Review of Some Recent Advances in Causal Inference (1/5)
    8. 21. A Review of Some Recent Advances in Causal Inference (2/5)
    9. 21. A Review of Some Recent Advances in Causal Inference (3/5)
    10. 21. A Review of Some Recent Advances in Causal Inference (4/5)
    11. 21. A Review of Some Recent Advances in Causal Inference (5/5)
  14. VIII. Targeted Learning
    1. 22. Targeted Learning for Variable Importance (1/4)
    2. 22. Targeted Learning for Variable Importance (2/4)
    3. 22. Targeted Learning for Variable Importance (3/4)
    4. 22. Targeted Learning for Variable Importance (4/4)
    5. 23. Online Estimation of the Average Treatment Effect (1/2)
    6. 23. Online Estimation of the Average Treatment Effect (2/2)
    7. 24. Mining with Inference: Data-Adaptive Target Parameters (1/3)
    8. 24. Mining with Inference: Data-Adaptive Target Parameters (2/3)
    9. 24. Mining with Inference: Data-Adaptive Target Parameters (3/3)
  15. Back Cover
3.142.199.191