Non-Gaussian Autoregressive-Type Time Series / by N. Balakrishna.

Format:

Book

Author/Creator:

Balakrishna, N., author.

Series:

Mathematics and Statistics Series

Language:

English

Subjects (All):

Time-series analysis.

Statistics.

Time Series Analysis.

Bayesian Inference.

Local Subjects:

Time Series Analysis.

Bayesian Inference.

Statistics.

Physical Description:

1 online resource (238 pages)

Edition:

1st ed. 2021.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2021.

Summary:

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.

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. .

Notes:

Includes bibliographical references.

Other Format:

Print version: Balakrishna, N. Non-Gaussian Autoregressive-Type Time Series

ISBN:

9789811681622

OCLC:

1294513600