| Document Type
|
:
|
BL
|
| Record Number
|
:
|
863820
|
| Title & Author
|
:
|
Handbook of Big Data Analytics /\ Wolfgang Karl Härdle, Henry Horng-Shing Lu, Xiaotong Shen, editors.
|
| Publication Statement
|
:
|
Cham, Switzerland :: Springer,, [2018]
|
|
|
:
|
, ©2018
|
| Series Statement
|
:
|
Springer handbooks of computational statistics
|
| Page. NO
|
:
|
1 online resource
|
| ISBN
|
:
|
3319182846
|
|
|
:
|
: 9783319182841
|
|
|
:
|
3319182838
|
|
|
:
|
9783319182834
|
| Bibliographies/Indexes
|
:
|
Includes bibliographical references.
|
| Contents
|
:
|
Intro; Preface; Contents; Part I Overview; 1 Statistics, Statisticians, and the Internet of Things; 1.1 Introduction; 1.1.1 The Internet of Things; 1.1.2 What Is Big Data in an Internet of Things?; 1.1.3 Building Blocks; 1.1.4 Ubiquity; 1.1.5 Consumer Applications; 1.1.6 The Internets of [Infrastructure] Things; 1.1.7 Industrial Scenarios; 1.2 What Kinds of Statistics Are Needed for Big IoT Data?; 1.2.1 Coping with Complexity; 1.2.2 Privacy; 1.2.3 Traditional Statistics Versus the IoT; 1.2.4 A View of the Future of Statistics in an IoT World; 1.3 Big Data in the Real World; 1.3.1 Skills.
|
|
|
:
|
1.3.2 Politics1.3.3 Technique; 1.3.4 Traditional Databases; 1.3.5 Cognition; 1.4 Conclusion; 2 Cognitive Data Analysis for Big Data; 2.1 Introduction; 2.1.1 Big Data; 2.1.2 Defining Cognitive Data Analysis; 2.1.3 Stages of CDA; 2.2 Data Preparation; 2.2.1 Natural Language Query; 2.2.2 Data Integration; 2.2.3 Metadata Discovery; 2.2.4 Data Quality Verification; 2.2.5 Data Type Detection; 2.2.6 Data Lineage; 2.3 Automated Modeling; 2.3.1 Descriptive Analytics; 2.3.2 Predictive Analytics; 2.3.3 Starting Points; 2.3.4 System Recommendations; 2.4 Application of Results; 2.4.1 Gaining Insights.
|
|
|
:
|
2.4.2 Sharing and Collaborating2.4.3 Deployment; 2.5 Use Case; 2.6 Conclusion; References; Part II Methodology; 3 Statistical Leveraging Methods in Big Data; 3.1 Background; 3.2 Leveraging Approximation for Least Squares Estimator; 3.2.1 Leveraging for Least Squares Approximation; 3.2.2 A Matrix Approximation Perspective; 3.2.3 The Computation of Leveraging Scores; 3.2.4 An Innovative Proposal: Predictor-Length Method; 3.2.5 More on Modeling; 3.2.6 Statistical Leveraging Algorithms in the Literature: A Summary; 3.3 Statistical Properties of Leveraging Estimator.
|
|
|
:
|
3.3.1 Weighted Leveraging Estimator3.3.2 Unweighted Leveraging Estimator; 3.4 Simulation Study; 3.4.1 UNIF and BLEV; 3.4.2 BLEV and LEVUNW; 3.4.3 BLEV and SLEV; 3.4.4 BLEV and PL; 3.4.5 SLEV and PL; 3.5 Real Data Analysis; 3.6 Beyond Linear Regression; 3.6.1 Logistic Regression; 3.6.2 Time Series Analysis; 3.7 Discussion and Conclusion; References; 4 Scattered Data and Aggregated Inference; 4.1 Introduction; 4.2 Problem Formulation; 4.2.1 Notations; 4.2.2 Review on M-Estimators; 4.2.3 Simple Averaging Estimator; 4.2.4 One-Step Estimator; 4.3 Main Results; 4.3.1 Assumptions.
|
|
|
:
|
4.3.2 Asymptotic Properties and Mean Squared Errors (MSE) Bounds4.3.3 Under the Presence of Communication Failure; 4.4 Numerical Examples; 4.4.1 Logistic Regression; 4.4.2 Beta Distribution; 4.4.3 Beta Distribution with Possibility of Losing Information; 4.4.4 Gaussian Distribution with Unknown Mean and Variance; 4.5 Discussion on Distributed Statistical Inference; 4.6 Other Problems; 4.7 Conclusion; References; 5 Nonparametric Methods for Big Data Analytics; 5.1 Introduction; 5.2 Classical Methods for Nonparametric Regression; 5.2.1 Additive Models; 5.2.2 Generalized Additive Models (GAM).
|
| Abstract
|
:
|
"Addressing a broad range of big data analytics in cross-disciplinary applications, this essential handbook focuses on the statistical prospects offered by recent developments in this field. To do so, it covers statistical methods for high-dimensional problems, algorithmic designs, computation tools, analysis flows and the software-hardware co-designs that are needed to support insightful discoveries from big data. The book is primarily intended for statisticians, computer experts, engineers and application developers interested in using big data analytics with statistics. Readers should have a solid background in statistics and computer science."--
|
| Subject
|
:
|
Big data-- Statistical methods.
|
| Subject
|
:
|
COMPUTERS-- Database-- General.
|
| Subject
|
:
|
Data mining.
|
| Subject
|
:
|
Data mining.
|
| Subject
|
:
|
Datos masivos.
|
| Subject
|
:
|
Mathematical statistical software.
|
| Subject
|
:
|
Maths for engineers.
|
| Subject
|
:
|
Probability statistics.
|
| Subject
|
:
|
Statistics.
|
| Dewey Classification
|
:
|
005.7
|
| LC Classification
|
:
|
QA76.9.B45H36 2018eb
|
| Added Entry
|
:
|
Härdle, Wolfgang
|
|
|
:
|
Lu, Henry Horng-Shing
|
|
|
:
|
Shen, Xiaotong,1964-
|