Asymptotic inference for stochastic processes

This is a survey of some aspects of large-sample inference for stochastic processes. A unified framework is used to study the asymptotic properties of tests and estimators parameters in discrete-time, continuous-time jump-type, and diffusion processes. Two broad families of processes, viz, ergodic and non-ergodic type are introduced and the qualitative differences in the asymptotic results for the two families are discussed and illustrated with several examples. Some results on estimation and testing via Bayesian, nonparametric, and sequential methods are also surveyed briefly.Maximun likelihood estimator likeliohood ratio and score tests ergodic and non-ergodic type processes jump type and diffusion processes asymptotic efficiency of tests and estimators Markov processes density estimation Bayes estimation and tests sequential methods