Please use this identifier to cite or link to this item: http://hdl.handle.net/10995/111202
Title: Forward and Reverse Representations for Markov Chains
Authors: Milstein, G. N.
Schoenmakers, J. G. M.
Spokoiny, V.
Issue Date: 2007
Publisher: Elsevier BV
Citation: Milstein G. N. Forward and Reverse Representations for Markov Chains / G. N. Milstein, J. G. M. Schoenmakers, V. Spokoiny // Stochastic Processes and their Applications. — 2007. — Vol. 117. — Iss. 8. — P. 1052-1075.
Abstract: In this paper we carry over the concept of reverse probabilistic representations developed in Milstein, Schoenmakers, Spokoiny [G.N. Milstein, J.G.M. Schoenmakers, V. Spokoiny, Transition density estimation for stochastic differential equations via forward-reverse representations, Bernoulli 10 (2) (2004) 281-312] for diffusion processes, to discrete time Markov chains. We outline the construction of reverse chains in several situations and apply this to processes which are connected with jump-diffusion models and finite state Markov chains. By combining forward and reverse representations we then construct transition density estimators for chains which have root-N accuracy in any dimension and consider some applications. © 2006 Elsevier Ltd. All rights reserved.
Keywords: ESTIMATION OF RISK
FORWARD AND REVERSE MARKOV CHAINS
MONTE CARLO SIMULATION
TRANSITION DENSITY ESTIMATION
COMPUTER SIMULATION
DISCRETE TIME CONTROL SYSTEMS
FINITE AUTOMATA
MONTE CARLO METHODS
PARAMETER ESTIMATION
PROBABILISTIC LOGICS
REVERSE ENGINEERING
RISK ASSESSMENT
ESTIMATION OF RISK
FORWARD AND REVERSE MARKOV CHAINS
JUMP DIFFUSION MODELS
TRANSITION DENSITY ESTIMATION
MARKOV PROCESSES
URI: http://hdl.handle.net/10995/111202
Access: info:eu-repo/semantics/openAccess
SCOPUS ID: 34347339641
ISSN: 0304-4149
metadata.dc.description.sponsorship: This research was supported by the Deutsche Forschungsgemeinschaft through the SFB 649 Economic Risk, and the DFG Research Center matheon in Berlin.
Appears in Collections:Научные публикации, проиндексированные в SCOPUS и WoS CC

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