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...
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| Otros Autores: | , |
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
2009.
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| Colección: | Probability and its Applications ;
Springer eBooks. |
| Acceso en línea: | Conectar con la versión electrónica |
| Ver en Universidad de Navarra: | https://innopac.unav.es/record=b32747597*spi |
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.
