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Non-gaussian autoregressive-type tim...

Balakrishna, N.

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Non-gaussian autoregressive-type time series

Record Type:	Electronic resources : Monograph/item
Title/Author:	Non-gaussian autoregressive-type time series/ by N. Balakrishna.
Author:	Balakrishna, N.
Published:	Singapore :Springer Singapore : : 2021.,
Description:	1 online resource (xviii, 225 p.) :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Time-series analysis. -
Online resource:	https://doi.org/10.1007/978-981-16-8162-2
ISBN:	9789811681622

Non-gaussian autoregressive-type time series
Balakrishna, N.

Non-gaussian autoregressive-type time series[electronic resource] /by N. Balakrishna. - Singapore :Springer Singapore :2021. - 1 online resource (xviii, 225 p.) :ill., digital ;24 cm.

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.

ISBN: 9789811681622

Standard No.: 10.1007/978-981-16-8162-2doiSubjects--Topical Terms:

532530
Time-series analysis.

LC Class. No.: QA280 / B35 2021

Dewey Class. No.: 519.55

Non-gaussian autoregressive-type time series
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245 1 0 $a Non-gaussian autoregressive-type time series $h [electronic resource] / $c by N. Balakrishna.
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300 $a 1 online resource (xviii, 225 p.) : $b ill., digital ; $c 24 cm.
505 0 $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.
520 $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.
650 0 $a Time-series analysis. $3 532530
650 1 4 $a Time Series Analysis. $3 3538821
650 2 4 $a Bayesian Inference. $3 3386929
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