Big Data Analytics and Information Science for Business and Biomedical Applications II
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 appl...
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| HerausgeberIn: | |
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| Year of Publication: | 2022 |
| Language: | English |
| Physical Description: | 1 electronic resource (196 p.) |
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| 245 | 1 | 0 | |a Big Data Analytics and Information Science for Business and Biomedical Applications II |
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| 520 | |a 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. | ||
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| 653 | |a bandwidth selection | ||
| 653 | |a correlation | ||
| 653 | |a edge-preserving image denoising | ||
| 653 | |a image sequence | ||
| 653 | |a jump regression analysis | ||
| 653 | |a local smoothing | ||
| 653 | |a nonparametric regression | ||
| 653 | |a spatio-temporal data | ||
| 653 | |a linear mixed model | ||
| 653 | |a ridge estimation | ||
| 653 | |a pretest and shrinkage estimation | ||
| 653 | |a multicollinearity | ||
| 653 | |a asymptotic bias and risk | ||
| 653 | |a LASSO estimation | ||
| 653 | |a high-dimensional data | ||
| 653 | |a big data adaptation | ||
| 653 | |a dividend estimation | ||
| 653 | |a options markets | ||
| 653 | |a weighted least squares | ||
| 653 | |a online health community | ||
| 653 | |a social support | ||
| 653 | |a network analysis | ||
| 653 | |a cancer | ||
| 653 | |a functional principal component analysis | ||
| 653 | |a functional predictor | ||
| 653 | |a linear mixed-effects model | ||
| 653 | |a mobile device | ||
| 653 | |a sparse group regularization | ||
| 653 | |a wearable device data | ||
| 653 | |a Bayesian modeling | ||
| 653 | |a functional regression | ||
| 653 | |a gestational weight | ||
| 653 | |a infant birth weight | ||
| 653 | |a joint modeling | ||
| 653 | |a longitudinal data | ||
| 653 | |a maternal weight gain | ||
| 653 | |a transfer learning | ||
| 653 | |a deep learning | ||
| 653 | |a pretrained neural networks | ||
| 653 | |a chest X-ray images | ||
| 653 | |a lung diseases | ||
| 653 | |a causal structure learning | ||
| 653 | |a consistency | ||
| 653 | |a FCI algorithm | ||
| 653 | |a high dimensionality | ||
| 653 | |a nonparametric testing | ||
| 653 | |a PC algorithm | ||
| 653 | |a fMRI | ||
| 653 | |a functional connectivity | ||
| 653 | |a brain network | ||
| 653 | |a Human Connectome Project | ||
| 653 | |a statistics | ||
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| 700 | 1 | |a Nathoo, Farouk |4 oth | |
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