Abstract
AbstractIn this paper, we develop methods of nonlinear filtering and interpolation of an unobservable Markov chain with a finite set of states. This Markov chain controls coefficients of AR(p) model. Using observations generated by AR(p) model we have to estimate the state of Markov chain in the case of an unknown probability transition matrix. To solve this problem we construct a system of equations with respect to the posterior probability of Markov states. According to the idea of empirical Bayes approach we represent these equations in the form independent of the unknown transition matrix. The resulting equations are solved using nonparametric kernel procedures by dependent observations. Comparison of proposed non-parametric algorithms with the optimal methods in the case of the known transition matrix is carried out by simulating.Keywordshidden Markov chainautoregressive modelnonlinear filtering and interpolationmultivariate kernel density estimation
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
