Handbook of Big Data Analytics

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Härdle, Wolfgang Karl (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Lu, Henry Horng-Shing (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Shen, Xiaotong (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2018.
• Έκδοση: 1st ed. 2018.
• Σειρά: Springer Handbooks of Computational Statistics,
• Θέματα: Statistics .; Data mining.; Applied mathematics.; Engineering mathematics.; Statistics and Computing/Statistics Programs.; Data Mining and Knowledge Discovery.; Mathematical and Computational Engineering.; Statistical Theory and Methods.; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.

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,...

Πίνακας περιεχομένων:

• 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)
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