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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...
| Cote: | Libro Electrónico |
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| Auteur principal: | |
| Collectivité auteur: | |
| Format: | Électronique eBook |
| Langue: | Inglés |
| Publié: |
New Delhi :
Springer India : Imprint: Springer,
2013.
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| Édition: | 1st ed. 2013. |
| Collection: | SpringerBriefs in Statistics,
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| Sujets: | |
| Accès en ligne: | Texto Completo |
Table des matières:
- 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.