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

• Otros Autores: Ahmed, S. Ejaz (Editor), Nathoo, Farouk (Editor)
• Formato: Electrónico Capítulo de libro
• Lenguaje: inglés
• Publicado: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
• Materias: Information technology industries; Computer science; 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
• Acceso en línea: DOAB: download the publication; DOAB: description of the publication

Sumario

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.

Descripción Física

1 electronic resource (196 p.)

ISBN

books978-3-0365-5550-8
9783036555492
9783036555508

Acceso

Open Access