Description

Book Synopsis

- 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.

Stochastic Models Statistics and Their Applications

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    A Hardback by Ansgar Steland

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      View other formats and editions of Stochastic Models Statistics and Their Applications by Ansgar Steland

      Publisher: Springer
      Publication Date: 24/08/2025
      ISBN13: 9783031960147, 978-3031960147
      ISBN10:
      Also in:
      Mathematics Probability and statistics

      Description

      Book Synopsis

      - 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.

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