Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-25T00:24:20.474Z Has data issue: false hasContentIssue false

FROM BOUNDARY CROSSING OF NON-RANDOM FUNCTIONS TO BOUNDARY CROSSING OF STOCHASTIC PROCESSES

Published online by Cambridge University Press:  17 April 2015

Mark Brown
Affiliation:
Department of Statistics, Columbia University, New York, NY 10027, USA E-mail: [email protected]
Victor de la Peña
Affiliation:
Department of Statistics, Columbia University, New York, NY 10027, USA E-mail: [email protected]
Tony Sit
Affiliation:
Department of Statistics, The Chinese University of Hong Kong, Hong Kong SAR E-mail: [email protected]

Abstract

One problem of wide interest involves estimating expected crossing-times. Several tools have been developed to solve this problem beginning with the works of Wald and the theory of sequential analysis. Deriving the explicit close form solution for the expected crossing times may be difficult. In this paper, we provide a framework that can be used to estimate expected crossing times of arbitrary stochastic processes. Our key assumption is the knowledge of the average behavior of the supremum of the process. Our results include a universal sharp lower bound on the expected crossing times. Furthermore, for a wide class of time-homogeneous, Markov processes, including Bessel processes, we are able to derive an upper bound E[a(Tr)]≤2r, which implies that sup r>0|((E[a(Tr)]−r)/r)|≤1, where a(t)=E[sup tXt] with {Xt}t≥0 be a non-negative, measurable process. This inequality motivates our claim that a(t) can be viewed as a natural clock for all such processes. The cases of multidimensional processes, non-symmetric and random boundaries are handled as well. We also present applications of these bounds on renewal processes in Example 10 and other stochastic processes.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Barlow, R., Bartholomew, D., Bremner, J. & Brunk, H. (1972). Statistical inference under order restrictions: theory and application of isotonic regression, New York: John Wiley and Sons.Google Scholar
2. Barlow, R. & Proschan, F. (1975). Statistical theory of reliability and life testing probability models. New York: Holt, Rinehart & Winston.Google Scholar
3. Brown, M. (2006). Exploiting the waiting time paradox: Applications of the size-biasing transformation. Probability in the Engineering and Informational Sciences 20: 195230.Google Scholar
4. Burkholder, D. & Gundy, R. (1970). Extrapolation and interpolation of quasi-linear operators on martingales. Acta Mathematica 124: 249304.Google Scholar
5. de la Peña, V. (1996). On Wald's equation and first exit times for randomly stopped processes with independent increments. Proceedings of conference Probability on Higher Dimensions in Progress in Probability 433: 277–286.Google Scholar
6. de la Peña, V. (1997). From boundary crossing of nonrandom functions to first passage times of processes with independent increments. Preprint, Department of Statistics, Columbia University, New York, NY.Google Scholar
7. de la Peña, V. & Eisenbaum, N. (1994). Decoupling inequalities for the local times of linear Brownian motion. Unpublished manuscript.Google Scholar
8. de la Peña, V. & Giné, E. (1999). Decoupling—From dependence to independence, New York: Springer.Google Scholar
9. de la Peña, V. & Govindarajulu, Z. (1992). A note on a second moment of a randomly stopped sum of independent variables. Statistics and Probability Letters 14: 275281.Google Scholar
10. de la Peña, V. & Yang, M. (2004). Bounding the first passage time on an average. Statistics and Probability Letters 67: 17.Google Scholar
11. Klass, M. (1988). A best possible improvement of Wald's equation: functions of sums of independent random variables. Annals of Probability 16: 413428.Google Scholar
12. Klass, M. (1990). Uniform lower bounds for randomly stopped Banach space-valued random sums. Annals of Probability 18: 790809.Google Scholar
13. Lai, T. (2001). Sequential analysis: Some classical problems and new challenges (with discussion and rejoinder). Statistica Sinica 11: 303408.Google Scholar
14. Rogers, L. & Williams, D. (2000). Diffusions, Markov processes and martingales. 2nd ed. Cambridge University Press: England.Google Scholar
15. Ross, S. (1996). Stochastic processes. 2nd ed. John Wiley and Sons, NY.Google Scholar
16. Sato, K. (2013). Lévy Processes and Infinitely Divisible Distributions, 2nd ed. Cambridge Studies in Advanced Mathematics.Google Scholar
17. Sehadri, V. (1994). The inverse Gaussian distribution: a case study in exponential families. Oxford University Press: New York.Google Scholar
18. Wald, A. (1945). Sequential tests of statistical hypotheses. Annals of Mathematical Statistics 16: 117186.Google Scholar
19. Wey, M. & Naud, C. (eds) (2011). On a new approach for estimating threshold crossing times with an application to global warming. http://giss.nasa.gov/meetings/cess2011, arXiv:1104.1580v2Google Scholar
20. Yang, M. (2002). Occupation times and beyond. Stochastic Processes and Their Applications 97: 7793.Google Scholar