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September, 1990 A Sufficient Condition for Asymptotic Sufficiency of Incomplete Observations of a Diffusion Process
Catherine F. Laredo
Ann. Statist. 18(3): 1158-1171 (September, 1990). DOI: 10.1214/aos/1176347744

Abstract

Consider an $m$-dimensional diffusion process $(X_t)$ with unknown drift and small known variance observed on a time interval $\lbrack 0, T\rbrack$. We derive here a general condition ensuring the asymptotic sufficiency, in the sense of Le Cam, of incomplete observations of $(X_t)_{0 \leq t \leq T}$ with respect to the complete observation of the diffusion as the variance goes to 0. We then construct estimators based on these partial observations which are consistent, asymptotically Gaussian and asymptotically equivalent to the maximum likelihood estimator based on the observation of the complete sample path on $\lbrack 0, T\rbrack$. Finally, we study when this condition is satisfied for various incomplete observations which often arise in practice: discrete observations, observations of a smoothed diffusion, observation of the first hitting times and positions of concentric spheres, complete or partial observation of the record process for one-dimensional diffusions.

Citation

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Catherine F. Laredo. "A Sufficient Condition for Asymptotic Sufficiency of Incomplete Observations of a Diffusion Process." Ann. Statist. 18 (3) 1158 - 1171, September, 1990. https://doi.org/10.1214/aos/1176347744

Information

Published: September, 1990
First available in Project Euclid: 12 April 2007

zbMATH: 0725.62073
MathSciNet: MR1062703
Digital Object Identifier: 10.1214/aos/1176347744

Subjects:
Primary: 62B15
Secondary: 62M05

Rights: Copyright © 1990 Institute of Mathematical Statistics

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Vol.18 • No. 3 • September, 1990
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