Statistical Inference for Discrete Time Stochastic Processes

This book offers an overview of statistical inference in stationary, discrete time stochastic processes. It reviews results in the last fifteen years, particularly on non-Gaussian sequences and semi-parametric and non-parametric analysis

The book deals with classical as well as most recent developments in the area of inference in discrete time stationary stochastic processes Topics discussed include Markov chains, non-Gaussian sequences, estimating function, density estimation and bootstrap for stationary observations and some of the results are available in a book form, most likely, for the first time The material is useful to research students and researchers working in the related areas Includes supplementary material: sn.pub/extras

Autorentext
M. B. Rajarshi received his Ph.D. in 1978 from the University of Pune, India. His research interests include inference for stochastic processes, applied probability and stochastic modeling. He has published about 35 papers in these areas mostly in international journals. Dr Rajarshi retired in 2009 from the University of Pune as a Professor of Statistics. He has held visiting appointments at Pennsylvania State University (USA), University of Waterloo and Memorial University of Newfoundland (Canada). He was the Chief Editor of the Journal of the Indian Statistical Association (2000-2006). He was elected as a Member of the International Statistical Institute (1998) and at present is the President of the India Chapter of the International Indian Statistician Association.

Inhalt
CAN Estimators from dependent observations.- Markov chains and their extensions.- Non-Gaussian ARMA models.- Estimating Functions.- Estimation of joint densities and conditional expectation.- Bootstrap and other resampling procedures.- Index.