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Spectral analysis of bilateral birth–death processes: some new explicit examples

Published online by Cambridge University Press:  15 June 2022

Manuel D. de la Iglesia*
Affiliation:
Universidad Nacional Autónoma de México
*
*Postal address: Instituto de Matemáticas, Universidad Nacional Autónoma de México, Circuito Exterior, C.U., 04510, Ciudad de México, Mexico. Email address: mdi29@im.unam.mx

Abstract

We consider the spectral analysis of several examples of bilateral birth–death processes and compute explicitly the spectral matrix and the corresponding orthogonal polynomials. We also use the spectral representation to study some probabilistic properties of the processes, such as recurrence, the invariant distribution (if it exists), and the probability current.

Type
Original Article
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of Applied Probability Trust

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References

Beckermann, B., Gilewicz, J. and Leopold, E. (1995). Recurrence relations with periodic coefficients and Chebyshev polynomials. Appl. Math. 23, 319323.Google Scholar
Chihara, T. S. (1968). An Introduction to Orthogonal Polynomials. Gordon and Breach, New York.Google Scholar
Conolly, B. W. (1971). On randomized random walks. SIAM Rev. 13, 8199.CrossRefGoogle Scholar
De la Iglesia, M. D. and Juarez, C. (2020). The spectral matrices associated with the stochastic Darboux transformations of random walks on the integers. J. Approximation Theory 258, 105458, 27 pp.CrossRefGoogle Scholar
Dette, H. and Reuther, B. (2010). Some comments on quasi-birth-and-death processes and matrix measures. J. Prob. Statist. 2010, 730543, 23 pp.CrossRefGoogle Scholar
Di Crescenzo, A., Iuliano, A. and Martinucci, B. (2012). On a bilateral birth–death process with alternating rates. Ric. Mat. 61, 157169.CrossRefGoogle Scholar
Di Crescenzo, A., Macci, C. and Martinucci, B. (2014). Asymptotic results for random walks in continuous time with alternating rates. J. Statist. Phys. 154, 13521364.CrossRefGoogle Scholar
Di Crescenzo, A. and Martinucci, B. (2009). On a symmetry, nonlinear birth–death process with bimodal transition probabilities. Symmetry 1, 201214.CrossRefGoogle Scholar
Domínguez de la Iglesia, M. (2021). Orthogonal Polynomials in the Spectral Analysis of Markov Processes: Birth–Death Models and Diffusion. Cambridge University Press.CrossRefGoogle Scholar
Dunford, N. and Schwartz, J. T. (1988). Linear Operators, Part II: Spectral Theory. John Wiley, New York.Google Scholar
Erdélyi, A., Magnus, W., Oberhettinger, F. and Tricomi, F. G. (1953). Higher Transcendental Functions, Vol. 2. McGraw-Hill, New York.Google Scholar
Gillespie, D. T. (1992). Markov Processes: An Introduction for Physical Scientists. Academic Press, Boston.Google Scholar
Giorno, V. and Nobile, A. G. (2019). First-passage times and related moments for continuous-time birth–death chains. Ric. Mat. 68, 629659.CrossRefGoogle Scholar
Giorno, V. and Nobile, A. G. (2020). On a class of birth–death processes with time-varying intensity functions. Appl. Math. Comput. 379, 125255, 24 pp.Google Scholar
Grünbaum, F. A. (2008). QBD processes and matrix orthogonal polynomials: some new explicit examples. In Numerical Methods for Structured Markov Chains (Dagstuhl Seminar Proceedings), eds D. Bini, B. Meini, V. Ramaswami, M. A. Remiche and P. Taylor, Schloss Dagstuhl—Leibniz-Zentrum für Informatik, Dagstuhl, 11 pp.Google Scholar
Grünbaum, F. A. and Velázquez, L. (2018). A generalization of Schur functions: applications to Nevanlinna functions, orthogonal polynomials, random walks and unitary and open quantum walks. Adv. Math. 326, 352464.CrossRefGoogle Scholar
Hongler, M. O. and Parthasarathy, P. R. (2008). On a super-diffusive, nonlinear birth and death process. Phys. Lett. A 372, 33603362.CrossRefGoogle Scholar
Ismail, M. E. H., Letessier, J., Masson, D. and Valent, G. (1990). Birth and death processes and orthogonal polynomials. In Orthogonal Polynomials, ed. P. Nevai, Springer, Dordrecht, pp. 229255.CrossRefGoogle Scholar
Karlin, S. and McGregor, J. (1957). The classification of birth-and-death processes. Trans. Amer. Math. Soc. 86, 366400.CrossRefGoogle Scholar
Karlin, S. and McGregor, J. (1957). The differential equations of birth and death processes, and the Stieltjes moment problem. Trans. Amer. Math. Soc. 85, 489546.CrossRefGoogle Scholar
Karlin, S. and McGregor, J. (1958). Linear growth, birth and death processes. J. Math. Mech. 7, 643662.Google Scholar
Karlin, S. and McGregor, J. (1958). Many server queueing processes with Poisson input and exponential service times. Pacific J. Math. 8, 87118.CrossRefGoogle Scholar
Karlin, S. and McGregor, J. (1959). Random walks. IIlinois J. Math. 3, 6681.Google Scholar
Kroese, D. P., Scheinhardt, W. R. W. and Taylor, P. G. (2004). Spectral properties of the tandem Jackson network, seen as a quasi-birth-and-death process. Ann. Appl. Prob. 14, 20572089.CrossRefGoogle Scholar
Latouche, G. and Ramaswami, V. (1999). Introduction to Matrix Analytic Methods in Stochastic Modeling. Society for Industrial and Applied Mathematics, Philadelphia.CrossRefGoogle Scholar
Neuts, M. F. (1989). Structured Stochastic Matrices of $M/G/1$ Type and Their Applications. Marcel Dekker, New York.Google Scholar
Parthasarathy, P. R. and Lenin, R. B. (2004). Birth and death process (BDP) models with applications—queueing, communication systems, chemical models, biological models: the state-of-the-art with a time-dependent perspective. Amer. J. Math. Manag. Sci. 24, 1212.Google Scholar
Pruitt, W. E. (1960). Bilateral birth and death processes. Tech. Rep., Applied Mathematics and Statistics Laboratories, Stanford University.Google Scholar
Pruitt, W. E. (1962). Bilateral birth and death processes. Trans. Amer. Math. Soc. 107, 508525.CrossRefGoogle Scholar
Schoutens, W. (2000). Stochastic Processes and Orthogonal Polynomials. Springer, New York.CrossRefGoogle Scholar
Schrijner, P. (1995). Quasi-stationarity of discrete-time Markov chains. Doctoral Thesis, Universiteit Twente.Google Scholar
Szegö, G. (1978). Orthogonal Polynomials, 4th edn. American Mathematical Society, Providence, RI.Google Scholar
Tarabia, A. M. K. and El-Baz, A. H. (2007). Transient solution of a random walk with chemical rule. Physica A 382, 430438.CrossRefGoogle Scholar
Van Assche, W. (1987). Asymptotics for Orthogonal Polynomials. Springer, Berlin, Heidelberg.CrossRefGoogle Scholar
Van Doorn, E. A. (1991). Quasi-stationary distributions and convergence for quasi-stationarity of birth–death processes. Adv. Appl. Prob. 23, 683700.CrossRefGoogle Scholar