# Penalized nonparametric drift estimation for a continuously observed one-dimensional diffusion process

Eva Löcherbach; Dasha Loukianova; Oleg Loukianov

ESAIM: Probability and Statistics (2012)

- Volume: 15, page 197-216
- ISSN: 1292-8100

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topLöcherbach, Eva, Loukianova, Dasha, and Loukianov, Oleg. "Penalized nonparametric drift estimation for a continuously observed one-dimensional diffusion process." ESAIM: Probability and Statistics 15 (2012): 197-216. <http://eudml.org/doc/222472>.

@article{Löcherbach2012,

abstract = {
Let X be a one dimensional positive recurrent diffusion continuously observed on [0,t] . We consider a non parametric estimator of the drift function on a given interval. Our estimator, obtained using a penalized least square approach, belongs to a finite dimensional functional space, whose dimension is selected according to the data. The non-asymptotic risk-bound reaches the minimax optimal rate of convergence when t → ∞. The main point of our work is that we do not suppose the process to be in stationary regime neither to be exponentially β-mixing. This is possible thanks to the use of a new polynomial inequality in the ergodic theorem [E. Löcherbach, D. Loukianova and O. Loukianov, Ann. Inst. H. Poincaré Probab. Statist.47 (2011) 425–449].},

author = {Löcherbach, Eva, Loukianova, Dasha, Loukianov, Oleg},

journal = {ESAIM: Probability and Statistics},

keywords = {Diffusion process; adaptive estimation; regeneration method; mean square estimator; model selection; deviation inequalities; diffusion process},

language = {eng},

month = {1},

pages = {197-216},

publisher = {EDP Sciences},

title = {Penalized nonparametric drift estimation for a continuously observed one-dimensional diffusion process},

url = {http://eudml.org/doc/222472},

volume = {15},

year = {2012},

}

TY - JOUR

AU - Löcherbach, Eva

AU - Loukianova, Dasha

AU - Loukianov, Oleg

TI - Penalized nonparametric drift estimation for a continuously observed one-dimensional diffusion process

JO - ESAIM: Probability and Statistics

DA - 2012/1//

PB - EDP Sciences

VL - 15

SP - 197

EP - 216

AB -
Let X be a one dimensional positive recurrent diffusion continuously observed on [0,t] . We consider a non parametric estimator of the drift function on a given interval. Our estimator, obtained using a penalized least square approach, belongs to a finite dimensional functional space, whose dimension is selected according to the data. The non-asymptotic risk-bound reaches the minimax optimal rate of convergence when t → ∞. The main point of our work is that we do not suppose the process to be in stationary regime neither to be exponentially β-mixing. This is possible thanks to the use of a new polynomial inequality in the ergodic theorem [E. Löcherbach, D. Loukianova and O. Loukianov, Ann. Inst. H. Poincaré Probab. Statist.47 (2011) 425–449].

LA - eng

KW - Diffusion process; adaptive estimation; regeneration method; mean square estimator; model selection; deviation inequalities; diffusion process

UR - http://eudml.org/doc/222472

ER -

## References

top- G. Banon, Nonparametric identification for diffusion processes. SIAM J. Control. Optim.16 (1978) 380–395.
- Y. Baraud, F. Comte and G. Viennet, Adaptive estimation in autoregression or β-mixing regression via model selection. Ann. Stat.29 (2001) 839–875.
- Y. Baraud, F. Comte and G. Viennet, Model selection for (auto-)regression with dependent data. ESAIM: P&S5 (2001) 33–49.
- A. Barron, L. Birgé and P. Massart, Risks bounds for model selection via penalization. Prob. Th. Rel. Fields113 (1999) 301–413.
- F. Comte, V. Genon-Catalot and Y. Rozenholc, Penalized nonparametric mean square estimation of the coefficients of diffusion processes. Bernoulli13 (2007) 514–543.
- A. Dalalyan, Sharp adaptive estimation of the drift function for ergodic diffusions. Ann. Stat.33 (2005) 507–2528.
- A.S. Dalalyan and Yu.A. Kutoyants, Asymptotically efficient trend coefficient estimation for ergodic diffusion. Math. Meth. Stat.11 (2002) 402–427.
- S. Delattre, M. Hoffmann and M. Kessler, Dynamics adaptive estimation of a scalar diffusion. Prpublication PMA-762, Univ. Paris 6 (2002). Available at www.proba.jussieu.fr/mathdoc/preprints/. Mathematical Reviews (MathSciNet): MR1895888 Project Euclid: euclid.bj/1078866865.
- L. Galtchouk and S. Pergamenschikov, Sequential nonparametric adaptive estimation of the drift coefficient in the diffusion processes. Math. Meth. Stat.10 (2001) 316–330.
- M. Hoffmann, Adaptive estimation in diffusion processes. Stochastic Processes Appl.79 (1999) 135–163.
- Yury A. Kutoyants, Statistical inference for ergodic diffusion processes. Springer Series in Statistics. London: Springer (2004).
- O. Lepskii, One problem of adaptive estimation in Gaussian white noise. Theory Probab. Appl.35 (1999) 459–470.
- G.G. Lorentz, M. von Golitschek and Y. Makovoz, Constructive approximation: advanced problems. Grundlehren der Mathematischen Wissenschaften 304. Berlin: Springer (1996).
- D. Loukianova and O. Loukianov, Uniform deterministic equivalent of additive functionals and non-parametric drift estimation for one-dimensional recurrent diffusions. Ann. Inst. Henri Poincaré 44 (2008) 771–786.
- E. Löcherbach and D. Loukianova, On Nummelin splitting for continuous time Harris recurrent Markov processes and application to kernel estimation for multi-dimensional diffusions. Stoch. Proc. Appl.118 (2008) 301–1321.
- E. Löcherbach, D. Loukianova and O. Loukianov, Polynomial bounds in the Ergodic theorem for one-dimensional diffusions and integrability of hitting times. Ann. Inst. H. Poincaré Probab. Statist.47 (2011) 425–449.
- T.D. Pham, Nonparametric estimation of the drift coefficient in the diffusion equation. Math. Operationsforsch. Statist., Ser. Statistics1 (1981) 61–73.
- B.L.S. Prakasa Rao, Statistical Inference for Diffusion Type Processes. London: Edward Arnold. MR1717690 (1999)
- D. Revuz and M. Yor, Continuous martingales and Brownian motion. 3rd ed. Grundlehren der Mathematischen Wissenschaften 293. Berlin: Springer (2005).
- V.G. Spokoiny, Adaptive drift estimation for nonparametric diffusion model. Ann. Stat.28 (2000) 815–836.
- A.Yu. Veretennikov, On polynomial mixing bounds for stochastic differential equations. Stoch. Proc. Appl.70 (1997) 115–127.

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