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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.

Humanities --- Social interaction --- 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

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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.

Humanities --- Social interaction --- 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 --- 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

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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.

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

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Effective environmental decision-making is often challenging and complex, where final solutions frequently possess inherently subjective political and socio-economic components. Consequently, complex sustainability applications in the “real world” frequently employ computational decision-making approaches to construct solutions to problems containing numerous quantitative dimensions and considerable sources of uncertainty. This volume includes a number of such applied computational analytics papers that either create new decision-making methods or provide innovative implementations of existing methods for addressing a wide spectrum of sustainability applications, broadly defined. The disparate contributions all emphasize novel approaches of computational analytics as applied to environmental decision-making and sustainability analysis – be this on the side of optimization, simulation, modelling, computational solution procedures, visual analytics, and/or information technologies.

streamflow forecasting --- C-vine copula --- quantile regression --- joint dependencies --- water resource management --- ecological relationship --- factorial analysis --- input-output analysis --- optimal path --- reduction --- urban solid waste system --- desalination --- reverse osmosis --- modelling --- simulation --- parameter estimation --- seawater --- boron --- watershed management --- nonpoint source pollution --- point source pollution --- water quality --- pollutant loadings --- South Texas --- eco-efficiency --- DEA --- CO2 emissions --- forecasting --- ecological indicators --- biomass gasification --- machine learning --- computer modeling --- computer simulation --- regression --- model reduction --- LASSO --- classification --- feature selection --- financial market --- investing --- sustainability --- renewable energy support --- energy modeling --- energy system design --- generation profile --- environmental footprint --- renewable energy --- electricity production --- unlisted companies --- Germany --- feed-in tariff --- biofuel policy --- investment profitability analysis --- the pay-off method --- simulation decomposition --- sourcing --- operational flexibility --- business aviation --- turboprop --- electric motor --- specific power --- Monte Carlo simulation --- Iowa food-energy-water nexus --- nitrogen export --- system modeling --- weather modeling --- optimal allocation --- interval --- fuzzy --- dynamic programming --- water resources --- n/a

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This reprint presents advances in operation and maintenance in solar plants, wind farms and microgrids. This compendium of scientific articles will help clarify the current advances in this subject, so it is expected that it will please the reader.

Research & information: general --- Physics --- wind turbine --- electric generator --- spectral analysis --- fault diagnosis --- photovoltaic power forecasting --- data-driven --- deep learning --- variational autoencoders --- RNN --- angle swinging --- grid frequency oscillations --- electromechanical system --- inertial masses --- microgrids --- coordination protection --- distributed generation --- photovoltaic resources --- DigSILENT --- photovoltaic module --- defect detection --- power plant --- efficiency --- thermal image --- photovoltaic aging --- dark I-V curves --- bidirectional power inverter --- online distributed measurement of dark I-V curves --- sustainability --- compressive strength --- Bolomey formula --- sustainable concrete --- glass powder --- solar cell --- solar panel --- parameter extraction --- analytical --- Lambert W-function --- spacecraft solar panels --- I-V curve --- modeling --- wind power --- non-conventional renewable energy --- forecasting --- energy bands --- combinatorial optimization --- deep learning (DL) --- unmanned aerial vehicle (UAV) --- photovoltaic (PV) systems --- image-processing --- image segmentation --- semantic segmentation --- faults diagnostic --- artificial intelligence --- unbalanced datasets --- synthetic data --- artificial neural network based MPPT --- hybrid boost converter --- renewable energies --- solar power system --- microgrid --- control system --- storage system --- primary control --- photovoltaic (PV) plants --- coverage path planning (CPP) --- corrosion monitoring --- FPGA --- offshore wind turbines --- ultrasound --- thickness loss --- SCADA --- visualisation --- software --- wind-turbine --- windfarm --- cross-platform --- HMI --- GUI --- corrosion --- monitoring --- photovoltaic systems --- expected energy models --- fleet-scale --- lasso regression --- performance modeling --- machine learning --- fault location in photovoltaic arrays --- failure modes simulation --- fault detection criterion --- adaptive protection --- distributed power generation --- power distribution --- power system protection --- wind turbine --- electric generator --- spectral analysis --- fault diagnosis --- photovoltaic power forecasting --- data-driven --- deep learning --- variational autoencoders --- RNN --- angle swinging --- grid frequency oscillations --- electromechanical system --- inertial masses --- microgrids --- coordination protection --- distributed generation --- photovoltaic resources --- DigSILENT --- photovoltaic module --- defect detection --- power plant --- efficiency --- thermal image --- photovoltaic aging --- dark I-V curves --- bidirectional power inverter --- online distributed measurement of dark I-V curves --- sustainability --- compressive strength --- Bolomey formula --- sustainable concrete --- glass powder --- solar cell --- solar panel --- parameter extraction --- analytical --- Lambert W-function --- spacecraft solar panels --- I-V curve --- modeling --- wind power --- non-conventional renewable energy --- forecasting --- energy bands --- combinatorial optimization --- deep learning (DL) --- unmanned aerial vehicle (UAV) --- photovoltaic (PV) systems --- image-processing --- image segmentation --- semantic segmentation --- faults diagnostic --- artificial intelligence --- unbalanced datasets --- synthetic data --- artificial neural network based MPPT --- hybrid boost converter --- renewable energies --- solar power system --- microgrid --- control system --- storage system --- primary control --- photovoltaic (PV) plants --- coverage path planning (CPP) --- corrosion monitoring --- FPGA --- offshore wind turbines --- ultrasound --- thickness loss --- SCADA --- visualisation --- software --- wind-turbine --- windfarm --- cross-platform --- HMI --- GUI --- corrosion --- monitoring --- photovoltaic systems --- expected energy models --- fleet-scale --- lasso regression --- performance modeling --- machine learning --- fault location in photovoltaic arrays --- failure modes simulation --- fault detection criterion --- adaptive protection --- distributed power generation --- power distribution --- power system protection