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

CULT

書目名稱Handbook of Big Data Analytics影響因子(影響力)




書目名稱Handbook of Big Data Analytics影響因子(影響力)學(xué)科排名




書目名稱Handbook of Big Data Analytics網(wǎng)絡(luò)公開度




書目名稱Handbook of Big Data Analytics網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Handbook of Big Data Analytics被引頻次




書目名稱Handbook of Big Data Analytics被引頻次學(xué)科排名




書目名稱Handbook of Big Data Analytics年度引用




書目名稱Handbook of Big Data Analytics年度引用學(xué)科排名




書目名稱Handbook of Big Data Analytics讀者反饋




書目名稱Handbook of Big Data Analytics讀者反饋學(xué)科排名

玩忽職守

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 ?sophagusvarizenblutung,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

技術(shù)

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 M?llers. 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

巨碩

安心地散步

Therapeutisches Arbeiten mit Tr?umendistributional 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

惡意

ANTH