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Empirical Finance

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ISBN: 9783038977063 Year: Pages: 276 DOI: 10.3390/books978-3-03897-707-0 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Economics
Added to DOAB on : 2019-04-05 10:34:31
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Abstract

There is no denying the role of empirical research in finance and the remarkable progress of empirical techniques in this research field. This Special Issue focuses on the broad topic of “Empirical Finance” and includes novel empirical research associated with financial data. One example includes the application of novel empirical techniques, such as machine learning, data mining, wavelet transform, copula analysis, and TV-VAR, to financial data. The Special Issue includes contributions on empirical finance, such as algorithmic trading, market efficiency, market microstructure, portfolio theory and asset allocation, asset pricing models, liquidity risk premium, currency crisis, return predictability, and volatility modeling.

Keywords

text similarity --- text mining --- machine learning --- SVM --- neural network --- LSTM --- credit risk --- ensemble learning --- deep learning --- bagging --- random forest --- boosting --- deep neural network --- causality-in-variance --- cross-correlation function --- housing and stock markets --- algorithmic trading --- take profit --- stop loss --- MACD --- ATR --- city banks --- dependence structure --- copula --- n/a --- market microstructure --- price discovery --- latency --- currency crisis --- random forests --- wavelet transform --- predictive accuracy --- housing price --- bank credit --- housing loans --- real estate development loans --- TVP-VAR model --- exchange rate --- volatility --- exports --- ARDL --- Vietnam --- crude oil futures prices forecasting --- convolutional neural networks --- short-term forecasting --- utility of international currency --- inertia --- liquidity risk premium --- US dollar --- Japanese yen --- cointegration --- statistical arbitrage --- natural gas --- wholesale electricity --- futures market --- spark spread --- earnings management --- earnings manipulation --- earnings quality --- initial public offering --- IPO --- asset pricing model --- data mining --- bankruptcy prediction --- financial and non-financial variables --- institutional investors’ shareholdings --- panel data model --- piecewise regression model --- global financial crisis --- gold return --- asymmetric dependence --- financial market stress --- robust regression --- quantile regression --- structural break --- flight to quality

Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing

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ISBN: 9783039212156 9783039212163 Year: Pages: 438 DOI: 10.3390/books978-3-03921-216-3 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Mechanical Engineering
Added to DOAB on : 2019-12-09 11:49:15
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As computer and space technologies have been developed, geoscience information systems (GIS) and remote sensing (RS) technologies, which deal with the geospatial information, have been rapidly maturing. Moreover, over the last few decades, machine learning techniques including artificial neural network (ANN), deep learning, decision tree, and support vector machine (SVM) have been successfully applied to geospatial science and engineering research fields. The machine learning techniques have been widely applied to GIS and RS research fields and have recently produced valuable results in the areas of geoscience, environment, natural hazards, and natural resources. This book is a collection representing novel contributions detailing machine learning techniques as applied to geoscience information systems and remote sensing.

Keywords

landslide --- bagging ensemble --- Logistic Model Trees --- GIS --- Vietnam --- colorization --- random forest regression --- grayscale aerial image --- change detection --- gully erosion --- environmental variables --- data mining techniques --- SCAI --- GIS --- mapping --- single-class data descriptors --- materia medica resource --- Panax notoginseng --- one-class classifiers --- geoherb --- change detection --- convolutional network --- deep learning --- panchromatic --- remote sensing --- remote sensing image segmentation --- convolutional neural networks --- Gaofen-2 --- hybrid structure convolutional neural networks --- winter wheat spatial distribution --- classification-based learning --- real-time precise point positioning --- convergence time --- ionospheric delay constraints --- precise weighting --- landslide --- weights of evidence --- logistic regression --- random forest --- hybrid model --- traffic CO --- traffic CO prediction --- neural networks --- GIS --- land use/land cover (LULC) --- unmanned aerial vehicle --- texture --- gray-level co-occurrence matrix --- machine learning --- crop --- landslide susceptibility --- random forest --- boosted regression tree --- information gain --- landslide susceptibility map --- ALS point cloud --- multi-scale --- classification --- large scene --- coarse particle --- particulate matter 10 (PM10) --- landsat image --- machine learning --- support vector machine --- high-resolution --- optical remote sensing --- object detection --- deep learning --- transfer learning --- land subsidence --- Bayes net --- naïve Bayes --- logistic --- multilayer perceptron --- logit boost --- change detection --- convolutional network --- deep learning --- panchromatic --- remote sensing --- leaf area index (LAI) --- machine learning --- Sentinel-2 --- sensitivity analysis --- training sample size --- spectral bands --- spatial sparse recovery --- constrained spatial smoothing --- spatial spline regression --- alternating direction method of multipliers --- landslide prediction --- machine learning --- neural networks --- model switching --- spatial predictive models --- predictive accuracy --- model assessment --- variable selection --- feature selection --- model validation --- spatial predictions --- reproducible research --- Qaidam Basin --- remote sensing --- TRMM --- artificial neural network --- n/a

Entropy Applications in Environmental and Water Engineering

Authors: --- ---
ISBN: 9783038972228 Year: Pages: 512 DOI: 10.3390/books978-3-03897-223-5 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Environmental Engineering --- General and Civil Engineering --- Technology (General)
Added to DOAB on : 2019-03-21 15:50:41
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Entropy theory has wide applications to a range of problems in the fields of environmental and water engineering, including river hydraulic geometry, fluvial hydraulics, water monitoring network design, river flow forecasting, floods and droughts, river network analysis, infiltration, soil moisture, sediment transport, surface water and groundwater quality modeling, ecosystems modeling, water distribution networks, environmental and water resources management, and parameter estimation. Such applications have used several different entropy formulations, such as Shannon, Tsallis, Reacutenyi Burg, Kolmogorov, Kapur, configurational, and relative entropies, which can be derived in time, space, or frequency domains. More recently, entropy-based concepts have been coupled with other theories, including copula and wavelets, to study various issues associated with environmental and water resources systems. Recent studies indicate the enormous scope and potential of entropy theory in advancing research in the fields of environmental and water engineering, including establishing and explaining physical connections between theory and reality. The objective of this Special Issue is to provide a platform for compiling important recent and current research on the applications of entropy theory in environmental and water engineering. The contributions to this Special Issue have addressed many aspects associated with entropy theory applications and have shown the enormous scope and potential of entropy theory in advancing research in the fields of environmental and water engineering.

Keywords

complexity --- streamflow --- water level --- composite multiscale sample entropy --- trend --- Poyang Lake basin --- four-parameter exponential gamma distribution --- principle of maximum entropy --- precipitation frequency analysis --- methods of moments --- maximum likelihood estimation --- flood frequency analysis --- generalized gamma (GG) distribution --- principle of maximum entropy (POME) --- entropy theory --- principle of maximum entropy (POME) --- GB2 distribution --- flood frequency analysis --- non-point source pollution --- ANN --- entropy weighting method --- data-scarce --- multi-events --- spatio-temporal variability --- soil water content --- entropy --- arid region --- joint entropy --- NDVI --- temperature --- precipitation --- groundwater depth --- Hei River basin --- turbulent flow --- canopy flow --- randomness --- coherent structures --- Shannon entropy --- Kolmogorov complexity --- entropy --- information transfer --- optimization --- radar --- rainfall network --- water resource carrying capacity --- forewarning model --- entropy of information --- fuzzy analytic hierarchy process --- projection pursuit --- accelerating genetic algorithm --- entropy production --- conditional entropy production --- stochastic processes --- scaling --- climacogram --- turbulence --- water resources vulnerability --- connection entropy --- changing environment --- set pair analysis --- Anhui Province --- cross-entropy minimization --- land suitability evaluation --- spatial optimization --- monthly streamflow forecasting --- Burg entropy --- configurational entropy --- entropy spectral analysis time series analysis --- entropy --- water monitoring --- network design --- hydrometric network --- information theory --- entropy applications --- hydrological risk analysis --- maximum entropy-copula method --- uncertainty --- Loess Plateau --- entropy --- water engineering --- Tsallis entropy --- principle of maximum entropy --- Lagrangian function --- probability distribution function --- flux concentration relation --- uncertainty --- information --- informational entropy --- variation of information --- continuous probability distribution functions --- confidence intervals --- precipitation --- variability --- marginal entropy --- crop yield --- Hexi corridor --- flow duration curve --- Shannon entropy --- entropy parameter --- modeling --- spatial and dynamics characteristic --- hydrology --- tropical rainfall --- statistical scaling --- Tsallis entropy --- multiplicative cascades --- Beta-Lognormal model --- rainfall forecast --- cross entropy --- ant colony fuzzy clustering --- combined forecast --- information entropy --- mutual information --- kernel density estimation --- ENSO --- nonlinear relation --- scaling laws --- power laws --- water distribution networks --- robustness --- flow entropy --- entropy theory --- frequency analysis --- hydrometeorological extremes --- Bayesian technique --- rainfall --- entropy ensemble filter --- ensemble model simulation criterion --- EEF method --- bootstrap aggregating --- bagging --- bootstrap neural networks --- El Niño --- ENSO --- neural network forecast --- sea surface temperature --- tropical Pacific --- entropy --- cross elasticity --- mean annual runoff --- water resources --- resilience --- quaternary catchment --- complement --- substitute --- entropy theory --- complex systems --- hydraulics --- hydrology --- water engineering --- environmental engineering

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MDPI - Multidisciplinary Digital Publishing Institute (3)


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CC by-nc-nd (3)


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english (3)


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2019 (3)