Big and complex data analysis : methodologies and applications /: methodologies and applications. ([2017])

• Record Type: Book
• Further Information: Note: S. Ejaz Ahmed, editor.
• Editors: Ahmed, S. E (Syed Ejaz), 1957-
• Contents: Preface; Contents; Part I General High-Dimensional Theory and Methods; Regularization After Marginal Learning for Ultra-High Dimensional Regression Models; 1 Introduction; 2 Model Setup and Several Methods in Variable Selection; 2.1 Model Setup and Notations; 2.2 Regularization Techniques; 2.3 Sure Independence Screening; 3 Regularization After Marginal Learning; 3.1 Algorithm; 3.2 Connections to SIS and RAR; 3.3 From RAM-2 to RAM; 4 Asymptotic Analysis; 4.1 Sure Independence Screening Property; 4.2 Sign Consistency for RAM-2; 4.3 Sign Consistency for RAM; 5 Numerical Study. 5.1 Tuning Parameter Selection5.2 Simulations; 6 Discussion; Appendix; References; Empirical Likelihood Test for High Dimensional Generalized Linear Models; 1 Introduction; 2 The Proposed Test; 3 The Partial Test with Nuisance Parameters; 4 Simulation Study; 5 Data Analysis; 6 Discussion; Appendix; References; Random Projections for Large-Scale Regression; 1 Introduction; 2 Theoretical Results; 3 Averaged Compressed Least Squares; 4 Discussion; Appendix; References; Testing in the Presence of Nuisance Parameters: Some Comments on Tests Post-Model-Selection and Random Critical Values. Bias-Reduced Moment Estimators of Population Spectral Distribution and Their Applications1 Introduction; 2 Moments of a PSD and Their Bias-Reduced Estimators; 3 Test Procedure; 4 Simulation; 4.1 Case of Testing for Order Two PSDs; 4.2 Case of Testing for Order Three PSDs; 5 Conclusions and Remarks; 6 Proofs; 6.1 Proof of Theorem 1; 6.2 Proof of Theorem 4; References; Part II Network Analysis and Big Data; Statistical Process Control Charts as a Tool for Analyzing Big Data; 1 Introduction; 2 Conventional SPC Charts; 3 Dynamic Statistical Screening; 4 Profiles/Images Monitoring. 5 Concluding RemarksReferences; Fast Community Detection in Complex Networks with a K-Depths Classifier; 1 Introduction; 2 Preliminaries and Background; 3 Community Detection Using L1 Data Depth; 3.1 Properties of Spectral Clustering K-Depths Algorithm; 4 Simulations; 4.1 Network Clustering with Two Groups; 4.2 Network Clustering with Outliers; 5 Application to Flickr Communities; 6 Conclusion and Future Work; References; How Different Are Estimated Genetic Networks of Cancer Subtypes?; 1 Introduction; 2 Network Reconstruction Methods; 2.1 Weighted Gene Correlation Network Analysis.
• Publisher Details: Cham, Switzerland : Springer
• Publication Date: 2017
• Extent: 1 online resource
• Subjects: 005.7; Statistics; Big data; Data mining; Mathematical statistics; Statistical methods; COMPUTERS -- Databases -- General; Big data; Data mining; Computers -- Mathematical & Statistical Software; Business & Economics -- Industries -- Computer Industry; Science -- Life Sciences -- General; Computers -- Database Management -- Data Mining; Mathematical & statistical software; Business mathematics & systems; Life sciences: general issues; Data mining; Mathematics -- Probability & Statistics -- General; Probability & statistics; Statistics; Statistical Theory and Methods; Statistics and Computing/Statistics Programs; Big Data/Analytics; Biostatistics; Data Mining and Knowledge Discovery; Electronic books
• Languages: English
• ISBNs: 9783319415734; 3319415735
• Related ISBNs: 9783319415727; 3319415727
• Notes: Note: Includes bibliographical references.; Note: Vendor-supplied metadata.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.