Non-Gaussian Autoregressive-Type Time Series
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Beschreibung
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.
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.
Brings together a variety of non-Gaussian autoregressive-type models to analyze time-series data Discusses the probabilistic and second-order properties of all models Reviews the models available for generating stationary time series on the positive support
Autor*in
N. Balakrishna
Themen in »Non-Gaussian Autoregressive-Type Time Series«
non Gaussian time series exponential autoregressive models laplace autoregressive models logistic autoregressive models Gamma autoregressive models autoregressive models with non Gaussian innovations autoregressive models with stable innovations time series models with slowly varying innovations minification models product autoregressive models estimating function methods quasi likelihood methods maximum probability estimators mixture autoregressive models Cauchy autoregressive models