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Autore: | Ahmed S. Ejaz |
Titolo: | Big Data Analytics and Information Science for Business and Biomedical Applications |
Pubblicazione: | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
Descrizione fisica: | 1 electronic resource (246 p.) |
Soggetto topico: | Humanities |
Social interaction | |
Soggetto non controllato: | 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 | |
Persona (resp. second.): | NathooFarouk |
AhmedS. Ejaz | |
Sommario/riassunto: | 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. |
Titolo autorizzato: | Big Data Analytics and Information Science for Business and Biomedical Applications |
Formato: | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione: | Inglese |
Record Nr.: | 9910557614803321 |
Lo trovi qui: | Univ. Federico II |
Opac: | Controlla la disponibilità qui |