Stochastic Models for Time Series
This book presents essential tools for modelling non-linear time series. The first part of the book describes the main standard tools of probability and statistics that directly apply to the time series context to obtain a wide range of modelling possibilities. Functional estimation and bootstrap ar...
Κύριος συγγραφέας: | |
---|---|
Συγγραφή απο Οργανισμό/Αρχή: | |
Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
Γλώσσα: | English |
Έκδοση: |
Cham :
Springer International Publishing : Imprint: Springer,
2018.
|
Έκδοση: | 1st ed. 2018. |
Σειρά: | Mathématiques et Applications,
80 |
Θέματα: | |
Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- Part I Independence and Stationarity
- 1 Probability and Independence
- 2 Gaussian convergence and inequalities
- 3 Estimation concepts
- 4 Stationarity
- Part II Models of time series
- 5 Gaussian chaos
- 6 Linear processes
- 7 Non-linear processes
- 8 Associated processes
- Part III Dependence
- 9 Dependence
- 10 Long-range dependence
- 11 Short-range dependence
- 12 Moments and cumulants
- Appendices
- A Probability and distributions
- B Convergence and processes
- C R scripts used for the gures
- Index- List of figures.