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

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书目名称Handbook of Big Data Analytics影响因子(影响力)




书目名称Handbook of Big Data Analytics影响因子(影响力)学科排名




书目名称Handbook of Big Data Analytics网络公开度




书目名称Handbook of Big Data Analytics网络公开度学科排名




书目名称Handbook of Big Data Analytics被引频次




书目名称Handbook of Big Data Analytics被引频次学科排名




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书目名称Handbook of Big Data Analytics年度引用学科排名




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书目名称Handbook of Big Data Analytics读者反馈学科排名

玩忽职守

A. Tarnawski,H. Gergely,T. G. Douglass advanced analytics. CDA is especially important in the age of big data, where the data is so complex, and includes both structured and unstructured data, that it is impossible to manually examine all possible combinations. As a cognitive computing system, CDA does not simply take over the entire pr

surrogate

https://doi.org/10.1007/978-3-658-25927-3cantly 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

ordain

,Therapie der Ösophagusvarizenblutung,ence is pursued. Distributed statistical inference is a technique to tackle a type of the above problem, and has recently attracted enormous attention. Many existing work focus on the averaging estimator, e.g., Zhang et al. (2013) and many others. In this chapter, we propose a one-step approach to e

技术

https://doi.org/10.1007/978-3-662-10458-3ta. 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

繁忙

Siegfried Kasper,Hans-Jürgen Möllers. A problem of current interest is clustering and classification of multiple time series. When various time series are fitted to models, the different time series can be grouped into clusters based on the fitted models. If there are different identifiable classes of time series, the fitted models c

巨硕

安心地散步

Therapeutisches Arbeiten mit Träumendistributional approximations of functionals of non-Gaussian vectors by those of Gaussian ones. Differently from the widely used Bonferroni approach, our procedure is dependence-adjusted and has an asymptotically correct size and power. To obtain cutoff values of our test, we propose a half-sampling

恶意

ANTH