Non-Gaussian Autoregressive-Type Time Series

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.