Titlebook: Big and Complex Data Analysis; Methodologies and Ap S. Ejaz Ahmed Book 2017 Springer International Publishing AG 2017 big data analysis.com

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Nonparametric Testing for Heterogeneous Correlationr a subpopulation. Two different testing procedures are compared. Both are based on the rankings of the values of two variables from a data set with a large number . of observations. The first maintains its level against Gaussian copulas; the second adapts to general alternatives in the sense that t

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Optimal Shrinkage Estimation in Heteroscedastic Hierarchical Linear Modelstion in the presence of linear predictors. We formulate two heteroscedastic hierarchical regression models and study optimal shrinkage estimators in each model. A class of shrinkage estimators, both parametric and semiparametric, based on unbiased risk estimate (URE) is proposed and is shown to be (

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lactic

High-Dimensional Classification for Brain Decodingctivity. In this setting the cognitive state is typically characterized by an element of a finite set, and the neuroimaging data comprise voluminous amounts of spatiotemporal data measuring some aspect of the neural signal. The associated statistical problem is one of the classifications from high-d

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Book 2017 high-dimensional data analysis. More concretely, its goals are to: 1) highlight and expand the breadth of existing methods in big data and high-dimensional data analysis and their potential for the advancement of both the mathematical and statistical sciences; 2) identify important directions for f

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hypotension

Free-Radical

How Different Are Estimated Genetic Networks of Cancer Subtypes?a simple permutation test for comparing estimated networks. The results provide new insight into properties of estimated networks using different reconstruction methods, as well as differences in estimated networks in different biological conditions.