Hostname: page-component-7bb8b95d7b-nptnm Total loading time: 0 Render date: 2024-09-12T20:49:06.702Z Has data issue: false hasContentIssue false

Functional limit theorems for stochastic processes based on embedded processes

Published online by Cambridge University Press:  01 July 2016

Richard F. Serfozo*
Affiliation:
Syracuse University

Abstract

The techniques used by Doeblin and Chung to obtain ordinary limit laws (central limit laws, weak and strong laws of large numbers, and laws of the iterated logarithm) for Markov chains, are extended to obtain analogous functional limit laws for stochastic processes which have embedded processes satisfying these laws. More generally, it is shown how functional limit laws of a stochastic process are related to those of a process embedded in it. The results herein unify and extend many existing limit laws for Markov, semi-Markov, queueing, regenerative, semi-stationary, and subordinated processes.

Type
Research Article
Copyright
Copyright © Applied Probability Trust 1975 

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] Aleskevicius, G. (1970) The central limit problem for sums of random variables defined on a Markov chain. Selected Transl. Math. Statist. and Prob. 9, 119126.Google Scholar
[2] Athreya, K. B. and Karlin, S. (1968) Embedding of urn schemes into continuous time Markov branching processes and related limit theorems. Ann. Math. Statist. 39, 18011817.CrossRefGoogle Scholar
[3] Billingsley, P. (1968) Convergence of Probability Measures. Wiley, New York.Google Scholar
[4] Bingham, N. (1971) Limit theorems for occupation times of Markov processes. Z. Wahrscheinlichkeitsth. 17, 122.CrossRefGoogle Scholar
[5] Bingham, N. H. (1972) Limit theorems for regenerative phenomena, recurrent events, and renewal theory. Z. Wahrscheinlichkeitsth. 21, 2044.Google Scholar
[6] Borovkov, A. (1967) On convergence of weakly dependent processes to a Wiener process. Theor. Probability Appl. 12, 193221.CrossRefGoogle Scholar
[7] Breiman, L. (1968) Probability. Addison-Wesley, Reading, Massachusetts.Google Scholar
[8] Chung, K. (1967) Markov Chains With Stationary Transition Probabilities, 2nd Ed. Springer-Verlag, New York.Google Scholar
[9] Cogburn, R. (1970) The central limit theorem for Markov processes. Proc. Sixth Berkeley Symp. Math. Statist. Prob. 485512.Google Scholar
[10] Darling, D. and Kac, M. (1957) On occupation times for Markov processes. Trans. Amer. Math. Soc. 79, 541555.Google Scholar
[11] Davydov, Y. (1970) The invariance principle for stationary processes. Theor. Probability Appl. 15, 487498.Google Scholar
[12] Dobrušin, R. (1956) Two limit theorems for the simple random walk on a line. Uspehi Mat. Nauk. 10, 139146.Google Scholar
[13] Doeblin, W. (1938) Sur deux problèmes de M. Kolmogorov concernant les chaînes dénombrables. Bull. Soc. Math. France 66, 210220.Google Scholar
[14] Dudley, R. (1968) Distances of probability measures and random variables. Ann. Math. Statist. 39, 15631572.Google Scholar
[15] Feller, W. (1971) An Introduction to Probability Theory and Its Applications 2, 2nd ed. Wiley, New York.Google Scholar
[16] Freedman, D. (1967) Some invariance principles for functionals of a Markov chain. Ann. Math. Statist. 38, 17.Google Scholar
[17] Freedman, D. (1971) Brownian Motion and Diffusion. Holden-Day, San Francisco.Google Scholar
[18] Fukushima, M. and Hitsuda, M. (1967) On a class of Markov processes taking values on lines and the central limit theorem. Nagoya Math. J. 30, 4756.CrossRefGoogle Scholar
[19] Gihman, I. I. and Skorohod, A. V. (1969) Introduction to the Theory of Random Processes. Saunders, Philadelphia.Google Scholar
[20] Gnedenko, B. and Fahim, G. (1969) On a transfer theorem. Soviet Math. Dokl. 10, 769772.Google Scholar
[21] Gnedenko, B. (1970) Limit theorems for sums of a random number of positive independent random variables. Proc. Sixth Berkeley Symp. Math. Statist. Prob. 537550.Google Scholar
[22] Guiasu, S. (1971) On the asymptotic distribution of the sequences of random variables with random indices. Ann. Math. Statist. 42, 20182028.CrossRefGoogle Scholar
[23] Hitsuda, M. and Shimizu, A. (1970) The central limit theorem for additive functional of Markov processes and weak convergence to Wiener measure. J. Math. Soc. Japan 22, 552566.Google Scholar
[24] Heyde, C. and Scott, D. (1973) Invariance principles for the law of the iterated logarithm for martingales and processes with stationary increments. Ann. Probability 1, 428436.CrossRefGoogle Scholar
[25] Ibragimov, I. A. (1962) Some limit theorems for stationary processes. Theor. Probability Appl. 7, 349382.Google Scholar
[26] Iglehart, D. (1971a) Multiple channel queues in heavy traffic IV: law of the iterated logarithm. Z. Wahrscheinlichkeitsth. 17, 168180.Google Scholar
[27] Iglehart, D. (1971b) Functional limit theorems for the queue GI/G/1 in light traffic. Adv. Appl. Prob. 3, 269281.Google Scholar
[28] Iglehart, D. and Kennedy, D. (1970) Weak convergence of the average of flag processes. J. Appl. Prob. 7, 747753.Google Scholar
[29] Iglehart, D. and Whitt, W. (1971) The equivalence of functional central limit theorems for counting processes and associated partial sums. Ann. Math. Statist. 42, 13721378.CrossRefGoogle Scholar
[30] Keilson, J. and Wishart, D. (1964) A central limit theorem for processes defined on a finite Markov chain. Proc. Camb. Phil. Soc. 60, 547567.Google Scholar
[31] Kesten, H. (1962) Occupation times for Markov and semi-Markov chains. Trans. Amer. Math. Soc. 103, 82112.Google Scholar
[32] Kimbleton, S. R. (1969) A stable limit theorem for Markov chains. Ann. Math. Statist. 40, 14671473.Google Scholar
[33] Kuelbs, J. and Kurtz, T. (1973) Berry-Esseen estimates in Hilbert space and an application to the law of the iterated logarithm. Technical Report, Department of Mathematics, University of Wisconsin.Google Scholar
[34] Lindvall, T. (1973) Weak convergence of probability measures and random functions in the function space D[0, ∞). J. Appl. Prob. 10, 109121.Google Scholar
[35] Philipp, W. (1969) The law of the iterated logarithm for mixing stochastic processes. Ann. Math. Statist. 40, 19851991.Google Scholar
[36] Philipp, W. and Webb, G. (1973) An invariance principle for mixing sequences of random variables. Z. Wahrscheinlichkeitsth. 25, 223237.Google Scholar
[37] Pinsky, M. (1968) Differential equations with a small parameter and a central limit theorem for functions defined on a finite Markov chain. Z. Wahrscheinlichkeitsth. 9, 101111.Google Scholar
[38] Priestly, M. B. (1965) Evolutionary spectra and non-stationary processes. J. R. Statist. Soc. B 27, 204237.Google Scholar
[39] Pyke, R. and Schaufele, R. A. (1964) Limit theorems for Markov renewal processes. Ann. Math. Statist. 35, 17461764.Google Scholar
[40] Pyke, R. (1969) Applications of almost surely convergent constructions of weakly convergent processes. Probability and Information Theory, 187200.Google Scholar
[41] Richter, W. (1965) Limit theorems for sequences of random variables with sequences of random indices. Theor. Probability Appl. 10, 7484.Google Scholar
[42] Rosenblatt, M. (1956) A central limit theorem and a mixing condition. Proc. Nat. Acad. Sci. USA 42, 4347.CrossRefGoogle Scholar
[43] Rosenblatt, M. (1971) Markov Processes: Structure and Asymptotic Behavior. Springer-Verlag, New York.Google Scholar
[44] Rozanov, Y. and Volkonski, V. (1969) Some limit theorems for random functions I. Theor. Probability Appl. 4, 178197.Google Scholar
[45] Schäl, M. (1970) Markov renewal processes with auxiliary paths. Ann. Math. Statist. 41, 16041623.Google Scholar
[46] Serfozo, R. F. (1972a) Processes with conditional stationary independent increments. J. Appl. Prob. 9, 303315.Google Scholar
[47] Serfozo, R. F. (1972b) Conditional Poisson processes. J. Appl. Prob. 9, 288302.Google Scholar
[48] Serfozo, R. F. (1972c) Semi-stationary processes. Z. Wahrscheinlichkeitsth. 23, 125132.Google Scholar
[49] Serfozo, R. F. (1973) Weak convergence of superpositions of randomly selected partial sums. Ann. Probability 1, 10441056.Google Scholar
[50] Silvestrov, D. (1969) Asymptotic behavior of first passage time for sums of random variables controlled by a regular semi-Markov process. Soviet Math. Dokl. 10, 15411543.Google Scholar
[51] Silvestrov, D. (1971a) On convergence of stochastic processes in the uniform topology. Soviet Math. Dokl. 12, 13351337.Google Scholar
[52] Silvestrov, D. (1971b) Limit theorems for functionals of integral type on diffusion processes. Soviet Math. Dokl. 12, 14501453.Google Scholar
[53] Silvestrov, D. (1972) The convergence of composite random functions in the J-topology. Soviet Math. Dokl. 13, 152154.Google Scholar
[54] Skorohod, A. (1956) Limit theorems for stochastic processes. Theor. Probability Appl. 1, 261290.Google Scholar
[55] Skorohod, A. V. (1965) Studies in the Theory of Random Processes. Addison-Wesley, Reading, Massachusetts.Google Scholar
[56] Straf, M. (1970) Weak convergence of stochastic processes with several parameters. Proc. Sixth Berkeley Symp. Math. Statist. Prob. 187221.Google Scholar
[57] Strassen, V. (1964) An invariance principle for the law of the iterated logarithm. Z. Wahrscheinlichkeitsth. 3, 211226.Google Scholar
[58] Vervaat, W. (1972) Functional central limit theorems for processes with positive drift and their inverses. Z. Wahrschenlichkeitsth. 23, 245253.Google Scholar
[59] Whitt, W. (1971a) Weak convergence theorems for priority queues: preemptive resume discipline. J. Appl. Prob. 8, 7494.Google Scholar
[60] Whitt, W. (1971b) Weak convergence of first passage time processes. J. Appl. Prob. 8, 417422.Google Scholar
[61] Whitt, W. (1974) Continuity of several functions of the function space D. Ann. Probability (to appear).Google Scholar
[62] Wichura, M. (1973) Some Strassen-type laws of the iterated logarithm for multiparameter stochastic processes with independent increments. Ann. Probability 1, 272296.Google Scholar