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On the Disorder Problem for a Negative Binomial Process

Published online by Cambridge University Press:  30 January 2018

Bruno Buonaguidi*
Affiliation:
Bocconi University
Pietro Muliere*
Affiliation:
Bocconi University
*
Postal address: Department of Decision Sciences, Bocconi University, Via Roentgen 1, 20136 Milan, Italy.
Postal address: Department of Decision Sciences, Bocconi University, Via Roentgen 1, 20136 Milan, Italy.
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Abstract

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We study the Bayesian disorder problem for a negative binomial process. The aim is to determine a stopping time which is as close as possible to the random and unknown moment at which a sequentially observed negative binomial process changes the value of its characterizing parameter p ∈ (0, 1). The solution to this problem is explicitly derived through the reduction of the original optimal stopping problem to an integro-differential free-boundary problem. A careful analysis of the free-boundary equation and of the probabilistic nature of the boundary point allows us to specify when the smooth fit principle holds and when it breaks down in favour of the continuous fit principle.

Type
Research Article
Copyright
© Applied Probability Trust 

References

Alili, L. and Kyprianou, A. E. (2005). Some remarks on first passage of Lévy processes, the American put and pasting principles. Ann. Appl. Prob. 15, 20622080.Google Scholar
Anscombe, F. J. (1950). Sampling theory of the negative binomial and logarithmic series distributions. Biometrika 37, 358382.Google Scholar
Barndorff-Nielsen, O. and Yeo, G. F. (1969). Negative binomial processes. J. Appl. Prob. 6, 633647.Google Scholar
Bayraktar, E. and Sezer, S. (2009). Online change detection for a Poisson process with a phase-type change-time prior distribution. Sequential Anal. 28, 218250.CrossRefGoogle Scholar
Buonaguidi, B. and Muliere, P. (2013a). Sequential testing problems for Lévy processes. Sequential Anal. 32, 4770.Google Scholar
Buonaguidi, B. and Muliere, P. (2013b). On the Wald's sequential probability ratio test for Lévy processes. Sequential Anal. 32, 267287.CrossRefGoogle Scholar
Carruthers, P. and Minh, D.-V. (1983). A connection between galaxy probabilities in Zwicky clusters counting distributions in particle physics and quantum optics. Phys. Lett. B 131, 116120.CrossRefGoogle Scholar
Dayanik, S. (2010). Compound Poisson disorder problems with nonlinear detection delay penalty cost functions. Sequential Anal. 29, 193216.Google Scholar
Dayanik, S. and Sezer, S. O. (2006). Compound Poisson disorder problem. Math. Operat. Res. 31, 649672.Google Scholar
Gapeev, P. V. (2005). The disorder problem for compound Poisson processes with exponential Jumps. Ann. Appl. Prob. 15, 487499.Google Scholar
Kozubowski, T. J. and Podgórski, K. (2009). Distributional properties of the negative binomial Lévy process. Prob. Math. Statist. 29, 4371.Google Scholar
Mukhopadhyay, N. (2014). Sequential sampling. In International Encyclopedia of Statistical Science, ed. Lovric, M., Springer, Berlin, pp. 13111314.Google Scholar
Mukhopadhyay, N. and de Silva, B. M. (2005). Two-stage estimation of mean in a negative binomial distribution with applications to Mexican bean beetle data. Sequential Anal. 24, 99137.CrossRefGoogle Scholar
Nedelman, J. (1983). A negative binomial model for sampling mosquitoes in a malaria survey. Biometrics 39, 10091020.Google Scholar
Peskir, G. and Shiryaev, A. N. (2002). Solving the Poisson disorder problem. In Advances in Finance and Stochastics, Springer, Berlin, pp. 295312.Google Scholar
Peskir, G. and Shiryaev, A. (2006). Optimal Stopping and Free-Boundary Problems. Birkhäuser, Basel.Google Scholar
Sato, K.-I. (1999). Lévy Processes and Infinitely Divisible Distributions. Cambridge University Press.Google Scholar
Shiryaev, A. N. (1978). Optimal Stopping Rules. Springer, New York.Google Scholar
Vaillant, J. (1991). Negative binomial distributions of individuals and spatio-temporal Cox processes. Scand. J. Statist. 18, 235248.Google Scholar
Wilson, L. T. and Room, P. M. (1983). Clumping patterns of fruit and arthropods in cotton, with implications for binomial sampling. Environmental Entomology 12, 5054.Google Scholar
Zhou, M. and Carin, L. (2013). Negative binomial process count and mixture modeling. IEEE Trans. Pattern Anal. Machine Intelligence, 99, 10.1109/TPAMI.2013.211.Google Scholar