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Field name	Details
Dewey Class	519.5
Title	Big and Complex Data Analysis ([EBook]) : Methodologies and Applications / / edited by S. Ejaz Ahmed.
Added Personal Name	Ahmed, S. Ejaz editor.
Other name(s)	SpringerLink (Online service)
Publication	Cham : : Springer International Publishing : : Imprint: Springer, , 2017.
Physical Details	XIV, 386 p. 85 illus., 55 illus. in color. : online resource.
Series	Contributions to Statistics 1431-1968
ISBN	9783319415734
Summary Note	This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Its peer-reviewed contributions showcase recent advances in variable selection, estimation and prediction strategies for a host of useful models, as well as essential new developments in the field. The continued and rapid advancement of modern technology now allows scientists to collect data of increasingly unprecedented size and complexity. Examples include epigenomic data, genomic data, proteomic data, high-resolution image data, high-frequency financial data, functional and longitudinal data, and network data. Simultaneous variable selection and estimation is one of the key statistical problems involved in analyzing such big and complex data. The purpose of this book is to stimulate research and foster interaction between researchers in the area of 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 future research in the theory of regularization methods, in algorithmic development, and in methodologies for different application areas; and 3) facilitate collaboration between theoretical and subject-specific researchers.:
Contents note	Preface -- Introduction -- Unsupervised Bump Hunting Using Principal Components -- Statistical Process Control Charts as a Tool for Analyzing Big Data -- Empirical Likelihood Test for High Dimensional Generalized Linear Models -- Identifying gene-environment interactions associated with prognosis using penalized quantile regression -- A Computationally Efficient Approach for Modeling Complex and Big Survival Data -- Regularization after marginal learning for ultra-high dimensional regression models -- Tests of concentration for low-dimensional and high-dimensional directional data -- Random Projections For Large-Scale Regression -- How Different are Estimated Genetic Networks of Cancer Subtypes? -- Analysis of correlated data with error-prone response under generalized linear mixed models -- High-Dimensional Classification for Brain Decoding -- Optimal shrinkage estimation in heteroscedastic hierarchical linear models -- Bias-reduced moment estimators of Population Spectral Distribution and their applications -- Testing in the Presence of Nuisance Parameters: Some Comments on Tests Post-Model-Selection and Random Critical Values -- A Mixture of Variance-Gamma Factor Analyzers -- Fast Community Detection in Complex Networks with a K-Depths Classifier.
System details note	Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users)
Internet Site	http://dx.doi.org/10.1007/978-3-319-41573-4
Links to Related Works	Subject References: Big Data . Big Data/Analytics . Biostatistics . Data mining . Data Mining and Knowledge Discovery . Statistical Theory and Methods . Statistics . Statistics and Computing/Statistics Programs . Authors: Ahmed, S. Ejaz . Corporate Authors: SpringerLink (Online service) . Series: Contributions to Statistics . Classification: 519.5 .

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Big and Complex Data Analysis: Methodologies and Applications

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