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Self-Normalized Processes Limit Theory and Statistical Applications /
Self-normalized processes are of common occurrence in probabilistic and statistical studies. A prototypical example is Student's t-statistic introduced in 1908 by Gosset, whose portrait is on the front cover. Due to the highly non-linear nature of these processes, the theory experienced a long...
| Clasificación: | Libro Electrónico |
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| Autores principales: | , , |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2009.
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| Edición: | 1st ed. 2009. |
| Colección: | Probability and Its Applications
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| Temas: | |
| Acceso en línea: | Texto Completo |
Tabla de Contenidos:
- Independent Random Variables
- Classical Limit Theorems, Inequalities and Other Tools
- Self-Normalized Large Deviations
- Weak Convergence of Self-Normalized Sums
- Stein's Method and Self-Normalized Berry-Esseen Inequality
- Self-Normalized Moderate Deviations and Laws of the Iterated Logarithm
- Cramér-Type Moderate Deviations for Self-Normalized Sums
- Self-Normalized Empirical Processes and U-Statistics
- Martingales and Dependent Random Vectors
- Martingale Inequalities and Related Tools
- A General Framework for Self-Normalization
- Pseudo-Maximization via Method of Mixtures
- Moment and Exponential Inequalities for Self-Normalized Processes
- Laws of the Iterated Logarithm for Self-Normalized Processes
- Multivariate Self-Normalized Processes with Matrix Normalization
- Statistical Applications
- The t-Statistic and Studentized Statistics
- Self-Normalization for Approximate Pivots in Bootstrapping
- Pseudo-Maximization in Likelihood and Bayesian Inference
- Sequential Analysis and Boundary Crossing Probabilities for Self-Normalized Statistics.