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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High-Dimensional Classificationmputation. In the past 15 years, several popular high-dimensional classifiers have been developed and studied in the literature. These classifiers can be roughly divided into two categories: sparse penalized margin-based classifiers and sparse discriminant analysis. In this chapter we give a compreh

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Analysis of High-Dimensional Regression Models Using Orthogonal Greedy AlgorithmsA) in high-dimensional sparse regression models with independent observations. In particular, when the regression coefficients are absolutely summable, the conditional mean squared prediction error and the empirical norm of OGA derived by Ing and Lai (Stat Sin 21:1473–1513, 2011) are introduced. We

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Inverse Modeling: A Strategy to Cope with Non-linearityenges 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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Bridging Density Functional Theory and Big Data Analytics with Applicationsregime. By technically mapping the data space into physically meaningful bases, the chapter provides a simple procedure to formulate global Lagrangian and Hamiltonian density functionals to circumvent the emerging challenges on large-scale data analyses. Then, the informative features of mixed datas

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