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.
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 witl£#
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