×

A change detection procedure for an ergodic diffusion process. (English) Zbl 1382.62044

Based on continuous time observations, a change point detection test is proposed for evaluating the evidence for a change in drift parameters of an ergodic diffusion process. The approach is based on establishing the asymptotic behavior of a random field relating to an estimating equation under the null hypothesis using weak convergence theory in separable Hilbert spaces.

MSC:

62M07 Non-Markovian processes: hypothesis testing
60J60 Diffusion processes
62M40 Random fields; image analysis
Full Text: DOI

References:

[1] Brodsky, B. E., Darkhovsky, B. S. (2000). Non-parametric statistical diagnosis. problems and methods. mathematics and its applications (Vol. 509). Dordrecht: Kluwer Academic Publishers. · Zbl 0995.62031
[2] Chen, J., Gupta, A. K. (2012). Parametric statistical change point analysis. With applications to genetics, medicine, and finance (2nd ed.). New York: Springer. · Zbl 1273.62016
[3] Csörgő, M., Horváth, L. (1997). Limit theorems in change-point analysis. Wiley series in probability and statistics. Chichester: Wiley. · Zbl 0884.62023
[4] De Gregorio, A., Iacus, S. M. (2008). Least squares volatility change point estimation for partially observed diffusion processes. Communications in Statistics-Theory and Methods, 37(15), 2342-2357. · Zbl 1144.62073
[5] Dehling, H., Franke, B., Kott, T., Kulperger, R. (2014). Change point testing for the drift parameters of a periodic mean reversion process. Statistical Inference for Stochastic Processes, 17(1), 1-18. · Zbl 1333.62194
[6] Horváth, L. (1993). The maximum likelihood method for testing changes in the parameters of normal observations. The Annals of Statistics, 21(2), 671-680. · Zbl 0778.62016 · doi:10.1214/aos/1176349143
[7] Horváth, L., Parzen, E. (1994). Limit theorems for fisher-score change processes. In: E. Carlstein, H.-G. Müller, D. Siegmund (Eds.) Change-point problems, IMS Lecture Notes—Monograph Series, 23, 157-169. · Zbl 1163.62346
[8] Khmaladze, E. V. (1979). The use of \[\omega^2\] ω2 tests for testing parametric hypothesis. Theory of Probability and Its Applications, 24(2), 283-301. · Zbl 0447.62049 · doi:10.1137/1124035
[9] Kutoyants, Y. A. (2004). Statistical inference for ergodic diffusion processes. Springer series in statistics. London: Springer. · Zbl 1038.62073
[10] Lánska, V. (1979). Minimum contrast estimation in diffusion processes. Journal of Applied Probability, 16(1), 65-75. · Zbl 0403.62060 · doi:10.1017/S0021900200046209
[11] LaRiccia, V., Mason, D. M. (1986). Cramér-von Mises statistics based on the sample quantile function and estimated parameters. Journal of Multivariate Analysis, 18(1), 93-106. · Zbl 0583.62015
[12] Lee, S., Nishiyama, Y., Yoshida, N. (2006). Test for parameter change in diffusion processes by cusum statistics based on one-step estimators. Annals of the Institute of Statistical Mathematics, 58(2), 211-222. · Zbl 1095.62100
[13] Mason, D. M. (1984). Weak convergence of the weighted empirical quantile process in \[L^2(0,1)\] L2(0,1). The Annals of Probability, 12(1), 243-255. · Zbl 0543.60010 · doi:10.1214/aop/1176993387
[14] Mihalache, S. (2012). Strong approximations and sequential change-point analysis for diffusion processes. Statistics & Probability Letters, 82(3), 464-472. · Zbl 1239.60024 · doi:10.1016/j.spl.2011.11.026
[15] Negri, I., Nishiyama, Y. (2012). Asymptotically distribution free test for parameter change in a diffusion process model. Annals of the Institute of Statistical Mathematics, 64(5), 911-918. · Zbl 1254.62089
[16] Negri, I., Nishiyama, Y. (2014). Z-process method for change point problems. Quaderni del Dipartimento di Ingegneria dell’informazione e metodi matematici. Serie “Matematica e Statistica” n. 5/MS- 2014. Dalmine: Universita degli studi di Bergamo. Retrieved from http://hdl.handle.net/10446/30761 · Zbl 0447.62049
[17] Nishiyama, Y. (2000). Entropy methods for martingales. CWI Tract, 128, Centrum voor Wiskunde en Informatica, Amsterdam. · Zbl 0949.60042
[18] Nishiyama, Y. (2011). Statistical analysis by the theory of martingales. (In Japanese). ISM Series, 1, Kindaikagakusha, Tokyo.
[19] Prokhorov, Y. V. (1956). Convergence of random processes and limit theorems in probability. Theory of Probability and Its Applications, 1(2), 157-214. · Zbl 0075.29001 · doi:10.1137/1101016
[20] Song, J., Lee, S. (2009). Test for parameter change in discretely observed diffusion processes. Statistical Inference for Stochastic Processes, 12(2), 165-183. · Zbl 1205.62116
[21] Suquet, Ch., Viano, M.-C. (1998). Change point detection in dependent sequences: invariance principles for some quadratic statistics. Mathematical Methods of Statistics, 7, 157-191. · Zbl 1103.62347
[22] Tsukuda, K., Nishiyama, Y. (2014). On \[L^2\] L2 space approach to change point problems. Journal of Statistical Planning and Inference, 149, 46-59. · Zbl 1432.62132
[23] van der Vaart, A. W. (1998). Asymptotic statistics. Cambridge: Cambridge University Press. · Zbl 0910.62001 · doi:10.1017/CBO9780511802256
[24] van der Vaart, A. W., Wellner, J. A. (1996). Weak convergence and empirical processes: with applications to statistics. New York: Springer. · Zbl 0862.60002
[25] van Zanten, H. (2003). On uniform laws of large numbers for ergodic diffusions and consistency of estimators. Statistical Inferences for Stochastic Processes, 6, 199-213. · Zbl 1036.60025 · doi:10.1023/A:1023904715206
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.