TY - JOUR
T1 - Parameter estimation of stochastic differential equation driven by small fractional noise
AU - Nakajima, Shohei
AU - Shimizu, Yasutaka
N1 - Funding Information:
The second author was partially supported by JSPS KAKENHI [grant number JP21K03358] and Japan Science and Technology Agency (JST) CREST [grant number JPMJCR14D7], Japan.
Publisher Copyright:
© 2022 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2022
Y1 - 2022
N2 - We study the problem of parametric estimation for continuously observed stochastic processes driven by additive small fractional Brownian motion with the Hurst index (Formula presented.). Under some assumptions on the drift coefficient, we obtain the asymptotic normality and moment convergence of maximum likelihood estimator of the drift parameter when a small dispersion coefficient (Formula presented.).
AB - We study the problem of parametric estimation for continuously observed stochastic processes driven by additive small fractional Brownian motion with the Hurst index (Formula presented.). Under some assumptions on the drift coefficient, we obtain the asymptotic normality and moment convergence of maximum likelihood estimator of the drift parameter when a small dispersion coefficient (Formula presented.).
KW - asymptotic normality
KW - fractional Brownian motion
KW - Parameter estimation
KW - small noise
KW - stochastic differential equation
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U2 - 10.1080/02331888.2022.2098960
DO - 10.1080/02331888.2022.2098960
M3 - Article
AN - SCOPUS:85123975948
SN - 0233-1888
VL - 56
SP - 919
EP - 934
JO - Statistics
JF - Statistics
IS - 4
ER -