Big Data Analytics and Information Science for Business and Biomedical Applications

doi:10.3390/books978-3-0365-3192-2

Download Url(s)

https://mdpi.com/books/pdfview/book/4975

Contributor(s)

Ahmed, S. Ejaz (editor)

Nathoo, Farouk (editor)

Language

English

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Abstract

The analysis of Big Data in biomedical as well as business and financial research has drawn much attention from researchers worldwide. This book provides a platform for the deep discussion of state-of-the-art statistical methods developed for the analysis of Big Data in these areas. Both applied and theoretical contributions are showcased.

URI

https://directory.doabooks.org/handle/20.500.12854/79592

Keywords

high-dimensional; nonlocal prior; strong selection consistency; estimation consistency; generalized linear models; high dimensional predictors; model selection; stepwise regression; deep learning; financial time series; causal and dilated convolutional neural networks; nuisance; post-selection inference; missingness mechanism; regularization; asymptotic theory; unconventional likelihood; high dimensional time-series; segmentation; mixture regression; sparse PCA; entropy-based robust EM; information complexity criteria; high dimension; multicategory classification; DWD; sparse group lasso; L2-consistency; proximal algorithm; abdominal aortic aneurysm; emulation; Medicare data; ensembling; high-dimensional data; Lasso; elastic net; penalty methods; prediction; random subspaces; ant colony system; bayesian spatial mixture model; inverse problem; nonparamteric boostrap; EEG/MEG data; feature representation; feature fusion; trend analysis; text mining