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Empirical Finance
| Empirical Finance |
| Autore | Hamori Shigeyuki |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
| Descrizione fisica | 1 electronic resource (276 p.) |
| Soggetto non controllato |
short-term forecasting
wavelet transform IPO volatility US dollar institutional investors’ shareholdings neural network financial market stress market microstructure text similarity TVP-VAR model Japanese yen convolutional neural networks global financial crisis deep neural network cross-correlation function boosting causality-in-variance flight to quality bagging earnings quality algorithmic trading stop loss statistical arbitrage ensemble learning liquidity risk premium gold return futures market take profit currency crisis spark spread city banks piecewise regression model financial and non-financial variables exports data mining latency crude oil futures prices forecasting random forests wholesale electricity SVM random forest bank credit deep learning Vietnam inertia MACD initial public offering text mining bankruptcy prediction exchange rate asset pricing model LSTM panel data model structural break credit risk housing and stock markets copula ARDL earnings manipulation machine learning natural gas housing price asymmetric dependence real estate development loans earnings management cointegration predictive accuracy robust regression quantile regression dependence structure housing loans price discovery utility of international currency ATR |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910346675203321 |
Hamori Shigeyuki
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| MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing
| Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing |
| Autore | Lee Saro |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
| Descrizione fisica | 1 electronic resource (438 p.) |
| Soggetto non controllato |
artificial neural network
model switching sensitivity analysis neural networks logit boost Qaidam Basin land subsidence land use/land cover (LULC) naïve Bayes multilayer perceptron convolutional neural networks single-class data descriptors logistic regression feature selection mapping particulate matter 10 (PM10) Bayes net gray-level co-occurrence matrix multi-scale Logistic Model Trees classification Panax notoginseng large scene coarse particle grayscale aerial image Gaofen-2 environmental variables variable selection spatial predictive models weights of evidence landslide prediction random forest boosted regression tree convolutional network Vietnam model validation colorization data mining techniques spatial predictions SCAI unmanned aerial vehicle high-resolution texture spatial sparse recovery landslide susceptibility map machine learning reproducible research constrained spatial smoothing support vector machine random forest regression model assessment information gain ALS point cloud bagging ensemble one-class classifiers leaf area index (LAI) landslide susceptibility landsat image ionospheric delay constraints spatial spline regression remote sensing image segmentation panchromatic Sentinel-2 remote sensing optical remote sensing materia medica resource GIS precise weighting change detection TRMM traffic CO crop training sample size convergence time object detection gully erosion deep learning classification-based learning transfer learning landslide traffic CO prediction hybrid model winter wheat spatial distribution logistic alternating direction method of multipliers hybrid structure convolutional neural networks geoherb predictive accuracy real-time precise point positioning spectral bands |
| ISBN | 3-03921-216-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910367564103321 |
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Lee Saro
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| MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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