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Nonidentifiability of the Two-State Markovian Arrival Process

Published online by Cambridge University Press:  14 July 2016

Pepa Ramírez-Cobo*
Affiliation:
CNRS
Rosa E. Lillo*
Affiliation:
Universidad Carlos III de Madrid
Michael P. Wiper*
Affiliation:
Universidad Carlos III de Madrid
*
Current address: Instituto Universitario de Matemáticas de la Universidad de Sevilla (IMUS), Facultad de Matemáticas, Universidad de Sevilla, Avda. Reina Mercedes, s/n, 41012 Sevilla, Spain. Email address: [email protected]
∗∗Postal address: Departamento de Estadística, Universidad Carlos III. C/ Madrid, 126 – 28903 Getafe (Madrid), Spain.
∗∗Postal address: Departamento de Estadística, Universidad Carlos III. C/ Madrid, 126 – 28903 Getafe (Madrid), Spain.
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Abstract

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In this paper we consider the problem of identifiability for the two-state Markovian arrival process (MAP2). In particular, we show that the MAP2 is not identifiable, providing the conditions under which two different sets of parameters induce identical stationary laws for the observable process.

Type
Research Article
Copyright
Copyright © Applied Probability Trust 2010 

References

[1] Asmussen, S. and Koole, G. (1993). Marked point processes as limits of Markovian arrival streams. J. Appl. Prob. 30, 365372.Google Scholar
[2] Ausín, M. C., Wiper, M. P. and Lillo, R. E. (2008). Bayesian prediction of the transient behaviour and busy period in short- and long-tailed GI/G/1 queueing systems. Comput. Statist. Data Anal. 52, 16151635.Google Scholar
[3] Bean, N. G. and Green, D. A. (2000). When is a MAP poisson? Math. Comput. Modelling 31, 3146.CrossRefGoogle Scholar
[4] Chakravarthy, S. R. (2001). The batch Markovian arrival process: a review and future work. In Advances in Probability Theory and Stochastic Processes, Notable Publications, NJ, pp. 2149.Google Scholar
[5] Fearnhead, P. and Sherlock, C. (2006). An exact Gibbs sampler for the Markov-modulated poisson process. J. R. Statist. Soc. B 68, 767784.Google Scholar
[6] Green, D. (1998). MAP/PH/1 departure processes. , School of Applied Mathematics, University of Adelaide.Google Scholar
[7] He, Q.-M. and Zhang, H. (2006). PH-invariant polytopes and Coxian representations of phase type distributions. Stoch. Models 22, 383409.Google Scholar
[8] He, Q.-M. and Zhang, H. (2008). An algorithm for computing minimal Coxian representations. INFORMS J. Computing 20, 179190.Google Scholar
[9] He, Q.-M. and Zhang, H. (2009). Coxian representations of generalized Erlang distributions. Acta Math. Appl. Sinica 25, 489502.Google Scholar
[10] Heffes, H. (1980). A class of data traffic processes-covariance function characterization and related queueing results. Bell Systems Tech. J. 59, 897929.Google Scholar
[11] Heffes, H. and Lucantoni, D. (1986). A Markov-modulated characterization of packetized voice and data traffic and related statistical multiplexer performance. IEEE J. Sel. Areas Commun. 4, 856868.Google Scholar
[12] Heyman, D. P. and Lucantoni, D. (2003). Modeling multiple IP traffic streams with rate limits. IEEE/ACM Trans. Networking 11, 948958.Google Scholar
[13] Ito, H., Armari, S.-I. and Kobayashi, K. (1992). Identifiability of hidden Markov information sources and their minimum degrees of freedom. IEEE Trans. Inf. Theory 38, 324333.CrossRefGoogle Scholar
[14] Leroux, B. G. (1992). Maximum-likelihood estimation for hidden Markov models. Stoch. Process. Appl. 40, 127143.Google Scholar
[15] Lucantoni, D. M. (1993). The BMAP/G/1 queue: a tutorial. In Performance Evaluation of Computer and Communication Systems, eds Donatiello, L. and Nelson, R., Springer, Berlin, pp. 330358.CrossRefGoogle Scholar
[16] Lucantoni, D. M., Meier-Hellstern, K. S. and Neuts, M. F. (1990). A single-server queue with server vacations and a class of nonrenewal arrival processes. Adv. Appl. Prob. 22, 676705.CrossRefGoogle Scholar
[17] Neuts, M. F. (1979). A versatile Markovian point process. J. Appl. Prob. 16, 764779.Google Scholar
[18] Nielsen, B. F. (2000). Modelling long-range dependent and heavy-tailed phenomena by matrix analytic methods. In Advances in Algorithmic Methods for Stochastic Models, Notable Publications, pp. 265278.Google Scholar
[19] Ramaswami, V. (1980). The N/G/1 queue and its detailed analysis. Adv. Appl. Prob. 12, 222261.Google Scholar
[20] Ramirez, P., Lillo, R. E. and Wiper, M. P. (2008). On identifiability of MAP processes. Technical Report, Statistics and Econometrics Working Papers ws084613, Universidad Carlos III de Madrid.Google Scholar
[21] Rydén, T. (1994). Consistent and asymptotically normal parameter estimates for hidden Markov models. Ann. Statist. 22, 18841895.Google Scholar
[22] Rydén, T. (1996). An EM algorithm for estimation in Markov-modulated Poisson processes. Comput. Statist. Data Anal. 21, 431447.Google Scholar
[23] Rydén, T. (1996). On identifiability and order of continous-time aggregated Markov chains, Markov-modulated Poisson processes, and phase-type distributions. J. Appl. Prob. 33, 640653.Google Scholar
[24] Scott, S. L. (1999). Bayesian analysis of a two-state Markov modulated Poisson process. J. Comput. Graph. Statist. 8, 662670.Google Scholar
[25] Scott, S. L. and Smyth, P. (2003). The Markov modulated Poisson process and Markov Poisson cascade with applications to web traffic modeling. In Bayesian Statistics, 7 (Tenerife, 2002), Oxford University Press, New York, pp. 671680.Google Scholar