Asymptotic Theory of Statistical Inference for Time Series /
The primary aims of this book are to provide modern statistical techniques and theory for stochastic processes. The stochastic processes mentioned here are not restricted to the usual AR, MA and ARMA processes. A wide variety of stochastic processes, e.g., non-Gaussian linear processes, long-memory...
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| Format: | eBook |
| Language: | English |
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New York, NY :
Springer New York,
2000.
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| Series: | Springer series in statistics.
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| Online Access: | Connect to the full text of this electronic book |
Table of Contents:
- Elements of Stochastic Processes
- Local Asymptotic Normality for Stochastic Processes
- Asymptotic Theory of Estimation and Testing for Stochastic Processes
- Higher Order Asymptotic Theory and Differential Geometry for Stochastic Processes
- Asymptotic Theory for Long-memory Processes
- Statistical Analysis Based on Functionals of Spectra
- Discriminant Analysis for Stationary Time Series
- Large Deviation Theory and Saddlepoint Approximation for Stochastic Processes.