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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.
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Paperback
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Discovery Miles 2 660
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