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Statistical Inference for Discrete Time Stochastic Processes
This work is an overview of statistical inference in stationary, discrete time stochastic processes. Results in the last fifteen years, particularly on non-Gaussian sequences and semi-parametric and non-parametric analysis have been reviewed. The first chapter gives a background of results on marti...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New Delhi :
Springer India : Imprint: Springer,
2013.
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| Edición: | 1st ed. 2013. |
| Colección: | SpringerBriefs in Statistics,
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| Temas: | |
| Acceso en línea: | Texto Completo |
Tabla de Contenidos:
- CAN Estimators from dependent observations
- Markov chains and their extensions
- Non-Gaussian ARMA models
- Estimating Functions
- Estimation of joint densities and conditional expectation
- Bootstrap and other resampling procedures
- Index.