Custom cover image
Custom cover image

Handbook of big data analytics / edited by Wolfgang Karl Härdle, Henry Horng-Shing Lu, Xiaotong Shen

Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Springer Handbooks of Computational Statistics | Springer eBook Collection | SpringerLink BücherPublisher: Cham : Springer International Publishing, 2018Description: Online-Ressource (VIII, 538 p. 147 illus., 109 illus. in color, online resource)ISBN:
  • 9783319182841
Subject(s): Genre/Form: Additional physical formats: 9783319182834 | Erscheint auch als: Handbook of big data analytics. Druck-Ausgabe. Cham, Switzerland : Springer, 2018. viii, 538 Seiten | Printed edition: 9783319182834 DDC classification:
  • 519.5
MSC: MSC: *62-00 | 62-07 | 68-00 | 68P01RVK: RVK: ST 530 | SK 830LOC classification:
  • QA276-280
DOI: DOI: 10.1007/978-3-319-18284-1Online resources: Summary: 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 scienceSummary: Preface -- Statistics, Statisticians, and the Internet of Things (John M. Jordan and Dennis K. J. Lin) -- Cognitive Data Analysis for Big Data (Jing Shyr, Jane Chu and Mike Woods) -- Statistical Leveraging Methods in Big Data (Xinlian Zhang, Rui Xie and Ping Ma) -- Scattered Data and Aggregated Inference (Xiaoming Huo, Cheng Huang and Xuelei Sherry Ni) -- Nonparametric Methods for Big Data Analytics (Hao Helen Zhang) -- Finding Patterns in Time Series (James E. Gentle and Seunghye J. Wilson) -- Variational Bayes for Hierarchical Mixture Models (Muting Wan, James G. Booth and Martin T. Wells) -- Hypothesis Testing for High-Dimensional Data (Wei Biao Wu, Zhipeng Lou and Yuefeng Han) -- High-Dimensional Classification (Hui Zou) -- Analysis of High-Dimensional Regression Models Using Orthogonal Greedy Algorithms (Hsiang-Ling Hsu, Ching-Kang Ing and Tze Leung Lai) -- Semi-Supervised Smoothing for Large Data Problems (Mark Vere Culp, Kenneth Joseph Ryan and George Michailidis) -- Inverse Modeling: A Strategy to Cope with Non-Linearity (Qian Lin, Yang Li and Jun S. Liu) -- Sufficient Dimension Reduction for Tensor Data (Yiwen Liu, Xin Xing and Wenxuan Zhong) -- Compressive Sensing and Sparse Coding (Kevin Chen and H. T. Kung) -- Bridging Density Functional Theory and Big Data Analytics with Applications (Chien-Chang Chen, Hung-Hui Juan, Meng-Yuan Tsai and Henry Horng-Shing Lu) -- Q3-D3-LSA: D3.js and generalized vector space models for Statistical Computing (Lukas Borke and Wolfgang Karl Härdle) -- A Tutorial on Libra: R Package for the Linearized Bregman Algorithm in High-Dimensional Statistics (Jiechao Xiong, Feng Ruan and Yuan Yao) -- Functional Data Analysis for Big Data: A Case Study on California Temperature Trends (Pantelis Zenon Hadjipantelis and Hans-Georg Müller) -- Bayesian Spatiotemporal Modeling for Detecting Neuronal Activation via Functional Magnetic Resonance Imaging (Martin Bezener, Lynn E. Eberly, John Hughes, Galin Jones and Donald R. Musgrove) -- Construction of Tight Frames on Graphs and Application to Denoising (Franziska Göbel, Gilles Blanchard and Ulrike von Luxburg) -- Beta-Boosted Ensemble for Big Credit Scoring Data (Maciej Zięba and Wolfgang Karl Härdle) --PPN: PPN: 1028034571Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-SMA | ZDB-2-SXMS
No physical items for this record

Reproduktion. (Springer eBook Collection. Mathematics and Statistics)

KIT – The Research University in the Helmholtz Association                         Home | Impressum | Datenschutz | Barrierefreiheit | KIT