Stochastic Models, Statistics and Their Applications, Gebunden

Klappentext

• 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.