Non-Gaussian Autoregressive-Type Time Series

This book provides a comprehensive overview of non-Gaussian autoregressive-type models for analyzing time-series data, covering model structure, probabilistic properties, and applications. It classifies stationary time-series models into different groups and discusses their properties and applications.

Format: Paperback / softback
Length: 225 pages
Publication date: 29 January 2023
Publisher: Springer Verlag, Singapore

This comprehensive volume explores a diverse range of non-Gaussian autoregressive-type models, designed to analyze time-series data. It serves as a valuable resource, gathering and consolidating the majority of existing models in this field, while also presenting their probabilistic and inferential characteristics. The book categorizes stationary time-series models into various groups, including linear stationary models with non-Gaussian innovations, linear stationary models with non-Gaussian marginal distributions, product autoregressive models, and minification models. While there are numerous non-Gaussian time-series models in the literature, a significant portion of them primarily emphasize the model structure and probabilistic properties.

The book begins by introducing the fundamental concepts and principles of non-Gaussian time-series models. It then delves into the theoretical foundations, describing the key assumptions and methodologies employed in these models. Following this, the book presents a comprehensive collection of non-Gaussian autoregressive-type models, encompassing a wide range of applications. Each model is thoroughly discussed, including its mathematical formulation, properties, and potential applications.

To enhance the understanding of these models, the book provides detailed examples and case studies. These examples illustrate how the models can be applied to real-world data sets, highlighting their strengths and limitations. Additionally, the book includes comprehensive discussions on the estimation and inference procedures for non-Gaussian time-series models. It covers topics such as maximum likelihood estimation, Bayesian inference, and empirical likelihood methods, providing practical guidance for researchers and practitioners.

Throughout the book, the authors emphasize the importance of understanding the underlying assumptions and characteristics of non-Gaussian time-series models. They highlight the significance of these models in various fields, such as finance, economics, meteorology, and biology. The book also provides insights into the challenges and limitations associated with non-Gaussian time-series analysis, encouraging researchers to develop new and improved models to address complex real-world problems.

In conclusion, this book is a valuable resource for researchers, practitioners, and students interested in non-Gaussian time-series analysis. It offers a comprehensive coverage of the field, presenting a diverse range of models and discussing their theoretical foundations, properties, and applications. By providing detailed examples and case studies, the book facilitates a deeper understanding of these models, enabling researchers to apply them effectively to real-world data sets. With its emphasis on understanding the underlying assumptions and characteristics of non-Gaussian time-series models, the book encourages the development of new and improved models to address complex real-world problems.

Weight: 379g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811681646
Edition number: 1st ed. 2021