scholarly journals Statistical methods for panel data from a semi-Markov process, with application to HPV

Biostatistics ◽  
2006 ◽  
Vol 8 (2) ◽  
pp. 252-264 ◽  
Author(s):  
M. Kang ◽  
S. W. Lagakos
Keyword(s):  
Panel Data ◽  
Markov Process ◽  
Statistical Methods ◽  
Semi Markov Process

Partially observable semi-Markov reward processes

1993 ◽  
Vol 30 (3) ◽  
pp. 548-560 ◽  
Author(s):  
Yasushi Masuda
Keyword(s):  
Markov Process ◽  
Counting Process ◽  
Probabilistic Interpretation ◽  
Real Domain ◽  
Semi Markov Process ◽  
Reward Process ◽  
Markov Reward ◽  
Partially Observable ◽  
Joint Behavior

The main objective of this paper is to investigate the conditional behavior of the multivariate reward process given the number of certain signals where the underlying system is described by a semi-Markov process and the signal is defined by a counting process. To this end, we study the joint behavior of the multivariate reward process and the multivariate counting process in detail. We derive transform results as well as the corresponding real domain expressions, thus providing clear probabilistic interpretation.

Continuous Semi-Markov Process by HARLAMOV, B.

Biometrics ◽  
2008 ◽  
Vol 64 (4) ◽  
pp. 1301-1301
Author(s):  
Mei-Jie Zhang
Keyword(s):  
Markov Process ◽  
Semi Markov Process

scholarly journals Maximal success durations for a semi-Markov process

1987 ◽  
Vol 24 (2) ◽  
pp. 203-224 ◽  
Author(s):  
David E. Fousler ◽  
Samuel Karlin
Keyword(s):  
Markov Process ◽  
Semi Markov Process

A note on the first two moments of times in transient states in a semi-Markov process

1974 ◽  
Vol 11 (01) ◽  
pp. 193-198 ◽  
Author(s):  
Edward P. C. Kao
Keyword(s):  
Markov Process ◽  
Transient States ◽  
Semi Markov Process

This paper derives results for computing the first two moments of times in transient states and times to absorption in a transient semi-Markov process. An illustrative example is presented at the end.

Partially observable semi-Markov reward processes

1993 ◽  
Vol 30 (03) ◽  
pp. 548-560 ◽  
Author(s):  
Yasushi Masuda
Keyword(s):  
Markov Process ◽  
Counting Process ◽  
Probabilistic Interpretation ◽  
Real Domain ◽  
Semi Markov Process ◽  
Reward Process ◽  
Markov Reward ◽  
Partially Observable ◽  
Joint Behavior

The main objective of this paper is to investigate the conditional behavior of the multivariate reward process given the number of certain signals where the underlying system is described by a semi-Markov process and the signal is defined by a counting process. To this end, we study the joint behavior of the multivariate reward process and the multivariate counting process in detail. We derive transform results as well as the corresponding real domain expressions, thus providing clear probabilistic interpretation.

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