Affiliations: Department of Mathematics, Northeastern University, Boston, MA, USA | E-mail: [email protected]
Corresponding author: Department of Mathematics, Northeastern University, Boston, MA, USA. E-mail: [email protected].
Abstract: Markov regime switching models remain enormously popular in speech recognition, economics, finance, etc. Nonparametric segmentation in switching models without probability assignment of jump moments is in many papers by Brodsky and Darkhovsky. We model all regimes as long SCOT strings. Stochastic COntext Tree (abbreviated as SCOT) is m-Markov Chain (m-MC) with every state of a string independent of the symbols in its more remote past than the context of length determined by the preceding symbols of this state. A parallel super-fast fitting and asymptotically optimal inference in a sparse SCOT model including the nonparametric homogeneity test are described in our previous papers. Our segmentation method is a combination of preliminary online change point detection with its subsequent offline Maximal Likelihood update.
Keywords: Strong mixing, SCOT emissions, Markov switching model, change point detection, maximum likelihood, HMM