The Annals of Statistics

A Sufficient Condition for Asymptotic Sufficiency of Incomplete Observations of a Diffusion Process

Catherine F. Laredo

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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.

Article information

Source
Ann. Statist., Volume 18, Number 3 (1990), 1158-1171.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176347744

Digital Object Identifier
doi:10.1214/aos/1176347744

Mathematical Reviews number (MathSciNet)
MR1062703

Zentralblatt MATH identifier
0725.62073

JSTOR
links.jstor.org

Subjects
Primary: 62B15: Theory of statistical experiments
Secondary: 62M05: Markov processes: estimation

Keywords
Diffusion process incomplete observations asymptotic sufficiency variance asymptotics parametric inference

Citation

Laredo, Catherine F. A Sufficient Condition for Asymptotic Sufficiency of Incomplete Observations of a Diffusion Process. Ann. Statist. 18 (1990), no. 3, 1158--1171. doi:10.1214/aos/1176347744. https://projecteuclid.org/euclid.aos/1176347744


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