Non-Gaussian Autoregressive-Type Time Series

N. Balakrishna (Autor)

Buch | Hardcover

225 Seiten

2022 | 1st ed. 2021
Springer Verlag, Singapore
978-981-16-8161-5 (ISBN)

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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.

N. BALAKRISHNA is a senior professor at the Department of Statistics and the director of International Relations at the Cochin University of Science and Technology (CUSAT), Cochin, Kerala. He joined CUSAT as a lecturer, in April 1992, after obtaining his M.Phil. and Ph.D. degrees in Statistics from the University of Pune, Maharashtra, India. He is an associate editor of several journals: Communications in Statistics: Theory and Methods, Simulation & Computation, and Journal of Indian Society for Probability and Statistics. He is also one of the editors-in-chief of the Journal of Indian Statistical Association. He is an elected member of the International Statistical Institute since 2005. Presently, he is the president of the Indian Society for Probability and Statistics (ISPS). As researcher in time-series analysis, Prof. Balakrishna received the UK-India Education and Research Initiative Fellowship, in 2007, to continue the research collaboration at University of Warwick. Earlier, he also received the Commonwealth Post-Doctoral Fellowship in 1999–2000 to do research at the University of Birmingham, UK. He was awarded the Distinguished Statistician Award by the Indian Society for Probability and Statistics, in 2018. Professor Balakrishna has published 55 research papers in refereed journals and successfully guided 10 scholars for their Ph.D. degree. Professor Balakrishna has visited several universities of the world: the University of Waterloo, Canada, to continue his ongoing research collaboration in time series; a visiting scientist at Technical University Dresden, Germany, during 2003–2004; and a visiting professor at Michigan State University, USA, during 2015–2016. He has attended several national and international conferences in India as well as abroad.

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.

Erscheinungsdatum	18.02.2022
Zusatzinfo	XVIII, 225 p.
Verlagsort	Singapore
Sprache	englisch
Maße	155 x 235 mm
Themenwelt	Mathematik / Informatik ► Mathematik ► Statistik
Themenwelt	Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
Schlagworte	autoregression • autoregressive models with non Gaussian innovations • autoregressive models with stable innovations • Cauchy autoregressive models • estimating function methods • exponential autoregressive models • Gamma autoregressive models • laplace autoregressive models • logistic autoregressive models • maximum probability estimators • minification models • mixture autoregressive models • Non Gaussian time series • product autoregressive models • quasi likelihood methods • time series models with slowly varying innovations
ISBN-10	981-16-8161-9 / 9811681619
ISBN-13	978-981-16-8161-5 / 9789811681615
Zustand	Neuware