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Big and complex data analysis. Methodologies and applications. (English) Zbl 1392.62007
Contributions to Statistics. Cham: Springer (ISBN 978-3-319-41572-7/hbk; 978-3-319-41573-4/ebook). xiv, 385 p. (2017).

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Publisher’s description: 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.
The articles of this volume will be reviewed individually.
Indexed articles:
Feng, Yang; Yu, Mengjia, Regularization after marginal learning for ultra-high dimensional regression models, 3-28 [Zbl 06815951]
Zang, Yangguang; Zhang, Qingzhao; Zhang, Sanguo; Li, Qizhai; Ma, Shuangge, Empirical likelihood test for high dimensional generalized linear models, 29-50 [Zbl 06815952]
Thanei, Gian-Andrea; Heinze, Christina; Meinshausen, Nicolai, Random projections for large-scale regression, 51-68 [Zbl 06815953]
Leeb, Hannes; Pötscher, Benedikt M., Testing in the presence of nuisance parameters: some comments on tests post-model-selection and random critical values, 69-82 [Zbl 1380.62089]
Yi, Grace Y.; Chen, Zhijian; Wu, Changbao, Analysis of correlated data with error-prone response under generalized linear mixed models, 83-102 [Zbl 06815955]
Qin, Yingli; Li, Weiming, Bias-reduced moment estimators of population spectral distribution and their applications, 103-119 [Zbl 1378.62090]
Qiu, Peihua, Statistical process control charts as a tool for analyzing big data, 123-138 [Zbl 1381.62294]
Tian, Yahui; Gel, Yulia R., Fast community detection in complex networks with a \(K\)-depths classifier, 139-157 [Zbl 1380.62231]
He, Kevin; Li, Yanming; Wei, Qingyi; Li, Yi, A computationally efficient approach for modeling complex and big survival data, 193-207 [Zbl 1383.62271]
Cutting, Christine; Paindaveine, Davy; Verdebout, Thomas, Tests of concentration for low-dimensional and high-dimensional directional data, 209-227 [Zbl 1381.62088]
Bamattre, Stephen; Hu, Rex; Verducci, Joseph S., Nonparametric testing for heterogeneous correlation, 229-246 [Zbl 1380.62187]
Kou, S. C.; Yang, Justin J., Optimal shrinkage estimation in heteroscedastic hierarchical linear models, 249-284 [Zbl 06815963]
Ahmed, Syed Ejaz; Yüzbaşı, Bahadır, High dimensional data analysis: integrating submodels, 285-304 [Zbl 06815964]
Díaz-Pachón, Daniel A.; Dazard, Jean-Eudes; Rao, J. Sunil, Unsupervised bump hunting using principal components, 325-345 [Zbl 1381.62096]
McNicholas, Sharon M.; McNicholas, Paul D.; Browne, Ryan P., A mixture of variance-gamma factor analyzers, 369-385 [Zbl 1381.62187]

62-06 Proceedings, conferences, collections, etc. pertaining to statistics
00B15 Collections of articles of miscellaneous specific interest
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