Description

Book Synopsis

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.



Table of Contents
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.

Non-Gaussian Autoregressive-Type Time Series

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    A Hardback by N. Balakrishna

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      Publisher: Springer Verlag, Singapore
      Publication Date: Publication Date: 28/01/2022
      ISBN13: 9789811681615, 978-9811681615
      ISBN10: 9811681619
      Also in:
      Bayesian inference

      Description

      Book Synopsis

      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.



      Table of Contents
      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.

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