LAMN property for hidden processes : the case of integrated diffusions
Annales de l'I.H.P. Probabilités et statistiques (2008)
- Volume: 44, Issue: 1, page 104-128
- ISSN: 0246-0203
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topGloter, Arnaud, and Gobet, Emmanuel. "LAMN property for hidden processes : the case of integrated diffusions." Annales de l'I.H.P. Probabilités et statistiques 44.1 (2008): 104-128. <http://eudml.org/doc/77957>.
@article{Gloter2008,
abstract = {In this paper we prove the Local Asymptotic Mixed Normality (LAMN) property for the statistical model given by the observation of local means of a diffusion process X. Our data are given by ∫01X(s+i)/n dμ(s) for i=0, …, n−1 and the unknown parameter appears in the diffusion coefficient of the process X only. Although the data are neither markovian nor gaussian we can write down, with help of Malliavin calculus, an explicit expression for the log-likelihood of the model, and then study the asymptotic expansion. We actually find that the asymptotic information of this model is the same one as for a usual discrete sampling of X.},
author = {Gloter, Arnaud, Gobet, Emmanuel},
journal = {Annales de l'I.H.P. Probabilités et statistiques},
keywords = {diffusion processes; parametric estimation; LAMN property; Malliavin calculus; non-markovian data; local asymptotic mixed normality; integrated diffusion processes; likelihood ratio; conditional information},
language = {eng},
number = {1},
pages = {104-128},
publisher = {Gauthier-Villars},
title = {LAMN property for hidden processes : the case of integrated diffusions},
url = {http://eudml.org/doc/77957},
volume = {44},
year = {2008},
}
TY - JOUR
AU - Gloter, Arnaud
AU - Gobet, Emmanuel
TI - LAMN property for hidden processes : the case of integrated diffusions
JO - Annales de l'I.H.P. Probabilités et statistiques
PY - 2008
PB - Gauthier-Villars
VL - 44
IS - 1
SP - 104
EP - 128
AB - In this paper we prove the Local Asymptotic Mixed Normality (LAMN) property for the statistical model given by the observation of local means of a diffusion process X. Our data are given by ∫01X(s+i)/n dμ(s) for i=0, …, n−1 and the unknown parameter appears in the diffusion coefficient of the process X only. Although the data are neither markovian nor gaussian we can write down, with help of Malliavin calculus, an explicit expression for the log-likelihood of the model, and then study the asymptotic expansion. We actually find that the asymptotic information of this model is the same one as for a usual discrete sampling of X.
LA - eng
KW - diffusion processes; parametric estimation; LAMN property; Malliavin calculus; non-markovian data; local asymptotic mixed normality; integrated diffusion processes; likelihood ratio; conditional information
UR - http://eudml.org/doc/77957
ER -
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