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- Leidėjas: Springer
- Metai: 2025
- Puslapiai: 208
- ISBN-10: 3031960149
- ISBN-13: 9783031960147
- Formatas: 16 x 24.1 x 1.7 cm, kieti viršeliai
- Kalba: Anglų
- Extra -25 % nuolaida šiai knygai su kodu: ENG25
Stochastic Models, Statistics and Their Applications (el. knyga) (skaityta knyga) | knygos.lt
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Aprašymas
- Part I: Stochastics and Statistical Theory.- Strong Gaussian Approximations with Random Multipliers.- Selection of Parametric Copula Models in the Approximation of Copulas using Cramér-von Mises Divergence.- Multivariate Dependence Based on Diagonal Sections: Spearman’s Footrule and Related Measures.- Proportional Asymptotics of Piecewise Exponential Proportional Hazards Models.- On the Choice of the Two Tuning Parameters for Nonparametric Estimation of an Elliptical Distribution Generator.- Part II: Inference and Machine Learning.- Inference from Longitudinal Data by Clustering and Machine Learning.- The Use of Neural Networks and PCA Dimensionality Reduction in the Imputation of Missing Fragments in High-Dimensional Time Series.- Discrete-Valued Time Series and Recurrent Neural Network Response Functions.- Application of Model-Free Time-Series Segmentation to Study Sleep in Mice.- Part III: Detection of Patterns in Data.- Testing for Dependence by Using Ordinal Patterns: Survey and Perspectives.- On Some Properties and Testing of Benford’s Law.
EXTRA 25 % nuolaida su kodu: ENG25
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- Leidėjas: Springer
- Metai: 2025
- Puslapiai: 208
- ISBN-10: 3031960149
- ISBN-13: 9783031960147
- Formatas: 16 x 24.1 x 1.7 cm, kieti viršeliai
- Kalba: Anglų
- Part I: Stochastics and Statistical Theory.- Strong Gaussian Approximations with Random Multipliers.- Selection of Parametric Copula Models in the Approximation of Copulas using Cramér-von Mises Divergence.- Multivariate Dependence Based on Diagonal Sections: Spearman’s Footrule and Related Measures.- Proportional Asymptotics of Piecewise Exponential Proportional Hazards Models.- On the Choice of the Two Tuning Parameters for Nonparametric Estimation of an Elliptical Distribution Generator.- Part II: Inference and Machine Learning.- Inference from Longitudinal Data by Clustering and Machine Learning.- The Use of Neural Networks and PCA Dimensionality Reduction in the Imputation of Missing Fragments in High-Dimensional Time Series.- Discrete-Valued Time Series and Recurrent Neural Network Response Functions.- Application of Model-Free Time-Series Segmentation to Study Sleep in Mice.- Part III: Detection of Patterns in Data.- Testing for Dependence by Using Ordinal Patterns: Survey and Perspectives.- On Some Properties and Testing of Benford’s Law.
Atsiliepimai