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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Sufficient Dimension Reduction for Tensor Data the number of samples. To preserve the tensor structure and reduce the dimensionality simultaneously, we revisit the tensor sufficient dimension reduction model and apply it to colorimetric sensor arrays. Tensor sufficient dimension reduction method is simple but powerful and exhibits a competent e

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Bridging Density Functional Theory and Big Data Analytics with Applications also applied on the post-process of magnetic resonance imaging (MRI) and better tumor recognitions can be achieved on the T1 post-contrast and T2 modes. It is appealing that the post-processing MRI using the proposed DFT-based algorithm would benefit the scientists in the judgment of clinical patho

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Q3-D3-LSA: D3.js and Generalized Vector Space Models for Statistical Computinger .. evaluation. QuantNet and the corresponding Data-Driven Documents (D3) based visualization can be found and applied under .. The driving technology behind it is Q3-D3-LSA, which is the combination of “GitHub API based QuantNet Mining infrastructure in .”, LSA and D3 implementation.

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https://doi.org/10.1007/978-3-662-10458-3ic methodologies, scalable algorithms, and the state-of-the-art computational tools have been designed for model estimation, variable selection, statistical inferences for high dimensional regression, and classification problems. This chapter provides an overview of recent advances on nonparametrics in big data analytics.

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Book 2018or 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. ?

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Therapie bakterieller Infektionene responses and the full feature data. This presentation is focused on local kernel regression methods in semi-supervised learning and provides a good starting point for understanding semi-supervised methods in general.

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Analysis of High-Dimensional Regression Models Using Orthogonal Greedy Algorithmsries models, and illustrate the advantage of our results compared to those established for Lasso by Basu and Michailidis (Ann Stat 43:1535–1567, 2015) and Wu and Wu (Electron J Stat 10:352–379, 2016).