Big Data Analytics and Information Science for Business and Biomedical Applications II

• Autore: Ahmed S. Ejaz
• Pubblicazione: Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022
• Descrizione fisica: 1 electronic resource (196 p.)
• Soggetto topico: Information technology industries; Computer science
• Soggetto non controllato: bandwidth selection; correlation; edge-preserving image denoising; image sequence; jump regression analysis; local smoothing; nonparametric regression; spatio-temporal data; linear mixed model; ridge estimation; pretest and shrinkage estimation; multicollinearity; asymptotic bias and risk; LASSO estimation; high-dimensional data; big data adaptation; dividend estimation; options markets; weighted least squares; online health community; social support; network analysis; cancer; functional principal component analysis; functional predictor; linear mixed-effects model; mobile device; sparse group regularization; wearable device data; Bayesian modeling; functional regression; gestational weight; infant birth weight; joint modeling; longitudinal data; maternal weight gain; transfer learning; deep learning; pretrained neural networks; chest X-ray images; lung diseases; causal structure learning; consistency; FCI algorithm; high dimensionality; nonparametric testing; PC algorithm; fMRI; functional connectivity; brain network; Human Connectome Project; statistics
• Persona (resp. second.): NathooFarouk; AhmedS. Ejaz
• Sommario/riassunto: The analysis of big data in biomedical, business and financial research has drawn much attention from researchers worldwide. This collection of articles aims to provide a platform for an in-depth discussion of novel statistical methods developed for the analysis of Big Data in these areas. Both applied and theoretical contributions to these areas are showcased.
• Titolo autorizzato: Big Data Analytics and Information Science for Business and Biomedical Applications II
• ISBN: 3-0365-5550-1
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910637784003321
• Lo trovi qui: Univ. Federico II