Название: Non-Gaussian Autoregressive-Type Time SeriesАвтор: N. Balakrishna
Издательство: Springer
Год: 2022
Страниц: 238
Язык: английский
Формат: pdf (true), epub
Размер: 19.8 MB
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. However, the validity of such models cannot be assessed without a valid inference method. One of the difficulties in dealing with non-Gaussian time series models is that, there is no unified approach to tackle the problem of estimation.
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