Titlebook: Handbook of Big Data Analytics; Wolfgang Karl H?rdle,Henry Horng-Shing Lu,Xiaotong Book 2018 Springer International Publishing AG, part of

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Sympathikusblockaden in der Praxis,enges in modern data analysis. Most forward regression modeling procedures are seriously compromised due to the curse of dimension. In this chapter, we show that the inverse modeling idea, originated from the . (SIR), can help us detect nonlinear relations effectively, and survey a few recent advanc

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E. Specker,H. Gülker,F. Bender,A. Theilmeiersing, and Internet search. How to extract useful information from massive data becomes the key issue nowadays. In spite of the urgent need for statistical tools to deal with such data, there are limited methods that can fully address the high-dimensional problem. In this chapter, we review the gener

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J. J. Herzberg,H. Hilmer,K. Wulfreproducibility and offering a platform for sharing validated knowledge native to the social web. To increase the information retrieval (IR) efficiency there is a need for incorporating semantic information. Three text mining models will be examined: vector space model (VSM), generalized VSM (GVSM),

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Statistical Leveraging Methods in Big Datacantly outpaces the increase of storage and computational capacity of high performance computers. The challenge in analyzing big data calls for innovative analytical and computational methods that make better use of currently available computing power. An emerging powerful family of methods for effe

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Nonparametric Methods for Big Data Analyticsta. Traditional nonparametric methods are challenged by modern high dimensional data due to the curse of dimensionality. Over the past two decades, there have been rapid advances in nonparametrics to accommodate analysis of large-scale and high dimensional data. A variety of cutting-edge nonparametr