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|a 9789811681622
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| 100 |
1 |
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|a Balakrishna, N.
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| 245 |
0 |
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|a Non-Gaussian Autoregressive-Type Time Series
|h Elektronische Ressource
|c by N. Balakrishna
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| 250 |
|
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|a 1st ed. 2021
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| 260 |
|
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|a Singapore
|b Springer Nature Singapore
|c 2021, 2021
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| 300 |
|
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|a XVIII, 225 p
|b online resource
|
| 505 |
0 |
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|a 1. Basics of Time Series -- 2. Statistical Inference for Stationary Time Series -- 3. AR Models with Stationary Non-Gaussian Positive Marginals -- 4. AR Models with Stationary Non-Gaussian Real-Valued Marginals -- 5. Some Nonlinear AR-type Models for Non-Gaussian Time series -- 6. Linear Time Series Models with Non-Gaussian Innovations -- 7. Autoregressive-type Time Series of Counts.
|
| 653 |
|
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|a Statistics
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| 653 |
|
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|a Bayesian Inference
|
| 653 |
|
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|a Time-series analysis
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| 653 |
|
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|a Time Series Analysis
|
| 653 |
|
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|a Statistics
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
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|b Springer
|a Springer eBooks 2005-
|
| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-981-16-8162-2?nosfx=y
|x Verlag
|3 Volltext
|
| 082 |
0 |
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|a 519.55
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| 520 |
|
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|a This book brings together a variety of non-Gaussian autoregressive-type models to analyze time-series data. This book collects and collates most of the available models in the field and provide their probabilistic and inferential properties. This book classifies the stationary time-series models into different groups such as linear stationary models with non-Gaussian innovations, linear stationary models with non-Gaussian marginal distributions, product autoregressive models and minification models. Even though several non-Gaussian time-series models are available in the literature, most of them are focusing on the model structure and the probabilistic properties
|