Nonparametric Estimation of the Stationary Distribution of a Discrete-Time Semi-Markov Process
DOI10.1080/03610926.2013.768666zbMath1320.60149OpenAlexW2005590454MaRDI QIDQ5265833
Stylianos Georgiadis, Nikolaos Limnios
Publication date: 29 July 2015
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610926.2013.768666
asymptotic propertiesstationary distributionnonparametric estimationdiscrete-time semi-Markov process
Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05) Non-Markovian processes: estimation (62M09) Markov renewal processes, semi-Markov processes (60K15)
Cites Work
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- Exact MLE and asymptotic properties for nonparametric semi-Markov models
- Ergodic theory for discrete semi-Markov chains
- Semi-Markov chains and hidden semi-Markov models toward applications. Their use in reliability and DNA analysis.
- Estimation of the stationary distribution of semi-Markov processes with Borel state space
- On the limiting distributions in Markov renewal processes with finitely many states
- Discrete-Time Semi-Markov Random Evolutions – Average and Diffusion Approximation of Difference Equations and Additive Functionals
- Stopped Random Walks
- On Discrete Time Semi-Markov Chains and Applications in Words Occurrences
- The Role of the Group Generalized Inverse in the Theory of Finite Markov Chains
- Semi-Markov Replacement Chains
- Empirical Estimator of Stationary Distribution for Semi-Markov Processes
- Discrete-Time Semi-Markov Model for Reliability and Survival Analysis
- Empirical estimation for discrete-time semi-Markov processes with applications in reliability
- Limit Theorems for Markov Renewal Processes
- Large Deviations for Empirical Estimators of the Stationary Distribution of a Semi-Markov Process with Finite State Space
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