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On reversible semi-Markov processes
Operations Research Letters, 1994zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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A characterization for mixtures of semi-Markov processes
Statistics & Probability Letters, 2002A stepped right-continuous random process with a countable space of states \(I\) is considerd. It can be represented by the random sequence \((\sigma_j, \xi_j)_1^\infty\), where \(\sigma_j\) is the \(j\)th jump time, and \(\xi_j\in I\) is the value of the process at time \(\sigma_j\). Let \(\nu_{im}\) be the \(m\)th hitting time of the state \(i\in I\),
EPIFANI, ILENIA +2 more
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Semi-Markov Replacement Chains
Advances in Applied Probability, 1994We consider an absorbing semi-Markov chain for which each time absorption occurs there is a resetting of the chain according to some initial (replacement) distribution. The new process is a semi-Markov replacement chain and we study its properties in terms of those of the imbedded Markov replacement chain.
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Functions of Semi-Markov Processes
SIAM Journal on Applied Mathematics, 1971A necessary and sufficient condition is presented under which a function of a semi-Markov process is again a semi-Markov process with transition probabilities which do not depend on the initial distribution of the original process. This result is a generalization of a known result for Markov processes.
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SIAM Journal on Control and Optimization, 2002
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Semi-Markov Disability Insurance Models
Communications in Statistics - Theory and Methods, 2013In this article, we present a stochastic model for disability insurance contracts. The model is based on a discrete time non homogeneous semi-Markov process (DTNHSMP) to which the backward recurrence time process is introduced. This permits a more exhaustive study of disability evolution and a more efficient approach to the duration problem. The use of
Guglielmo D'Amico +2 more
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A semi-markov model for clinical trials
Journal of Applied Probability, 1965This paper applies the theory of semi-Markov processes to the construction of a stochastic model for interpreting data obtained from clinical trials. The model characterizes the patient as being in one of a finite number of states at any given time with an arbitrary probability distribution to describe the length of stay in a state. Transitions between
Weiss, G. H., Zelen, M.
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1996
In Section 4.2, we said that for a homogeneous, continuous-parameter Markov chain, the sojourn time (the amount of time in a state) is exponentially distributed. If we lift this restriction and allow the sojourn time to be any distribution function, the process is called a semi-Markov process (SMP). In a semi-Markov process, the rate of transition from
Robin Sahner +2 more
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In Section 4.2, we said that for a homogeneous, continuous-parameter Markov chain, the sojourn time (the amount of time in a state) is exponentially distributed. If we lift this restriction and allow the sojourn time to be any distribution function, the process is called a semi-Markov process (SMP). In a semi-Markov process, the rate of transition from
Robin Sahner +2 more
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2001
Let Q(x,A,t), x ∈ E, A ∈ e,t ∈ IR+, be a semi-Markov kernel on (E,e) and let (J n ,S n )n∈N and (J n ,X n )n∈N be, respectively, the associated MRP and the (J-X)-process (see Section 2.2).
N. Limnios, G. Oprişan
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Let Q(x,A,t), x ∈ E, A ∈ e,t ∈ IR+, be a semi-Markov kernel on (E,e) and let (J n ,S n )n∈N and (J n ,X n )n∈N be, respectively, the associated MRP and the (J-X)-process (see Section 2.2).
N. Limnios, G. Oprişan
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