---

＜E-Book＞
Non-Gaussian Autoregressive-Type Time Series / by N. Balakrishna

Edition	1st ed. 2021.
Publisher	(Singapore : Springer Nature Singapore : Imprint: Springer)
Year	2021
Size	XVIII, 225 p : online resource
Authors	*Balakrishna, N author SpringerLink (Online service)
Subjects	LCSH:Time-series analysis LCSH:Statistics  FREE:Time Series Analysis FREE:Bayesian Inference FREE:Statistics
Notes	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. 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 HTTP:URL=https://doi.org/10.1007/978-981-16-8162-2

 Similar Items