Balakrishna Non-Gaussian Autoregressive-Type Time Series
1. Auflage 2022
ISBN: 978-981-16-8162-2
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark
E-Book, Englisch, 225 Seiten
Reihe: Mathematics and Statistics (R0)
ISBN: 978-981-16-8162-2
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark
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.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
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.




