Parameter estimation of Ornstein–Uhlenbeck process generating a stochastic graph

Emmanuel Gobet; Gustaw Matulewicz

doi:10.1007/s11203-016-9142-4

Parameter estimation of Ornstein–Uhlenbeck process generating a stochastic graph

Emmanuel Gobet
, Gustaw Matulewicz

Université Paris-Saclay

Research output: Contribution to journal › Article › peer-review

Abstract

Given Y a graph process defined by an incomplete information observation of a multivariate Ornstein–Uhlenbeck process X, we investigate whether we can estimate the parameters of X. We define two statistics of Y. We prove convergence properties and show how these can be used for parameter inference. Finally, numerical tests illustrate our results and indicate possible extensions and applications.

Original language	English
Pages (from-to)	211-235
Number of pages	25
Journal	Statistical Inference for Stochastic Processes
Volume	20
Issue number	2
DOIs	https://doi.org/10.1007/s11203-016-9142-4
Publication status	Published - 1 Jul 2017
Externally published	Yes

Keywords

Asymptotic properties of estimators
Incomplete information
Inference for stochastic process
Stochastic graph process

Access to Document

10.1007/s11203-016-9142-4

Cite this

@article{a0fb91be78ae4cd290dfcb17ddc5ba67,

title = "Parameter estimation of Ornstein–Uhlenbeck process generating a stochastic graph",

abstract = "Given Y a graph process defined by an incomplete information observation of a multivariate Ornstein–Uhlenbeck process X, we investigate whether we can estimate the parameters of X. We define two statistics of Y. We prove convergence properties and show how these can be used for parameter inference. Finally, numerical tests illustrate our results and indicate possible extensions and applications.",

keywords = "Asymptotic properties of estimators, Incomplete information, Inference for stochastic process, Stochastic graph process",

author = "Emmanuel Gobet and Gustaw Matulewicz",

note = "Publisher Copyright: {\textcopyright} 2016, Springer Science+Business Media Dordrecht.",

year = "2017",

month = jul,

day = "1",

doi = "10.1007/s11203-016-9142-4",

language = "English",

volume = "20",

pages = "211--235",

journal = "Statistical Inference for Stochastic Processes",

issn = "1387-0874",

number = "2",

}

TY - JOUR

T1 - Parameter estimation of Ornstein–Uhlenbeck process generating a stochastic graph

AU - Gobet, Emmanuel

AU - Matulewicz, Gustaw

PY - 2017/7/1

Y1 - 2017/7/1

N2 - Given Y a graph process defined by an incomplete information observation of a multivariate Ornstein–Uhlenbeck process X, we investigate whether we can estimate the parameters of X. We define two statistics of Y. We prove convergence properties and show how these can be used for parameter inference. Finally, numerical tests illustrate our results and indicate possible extensions and applications.

AB - Given Y a graph process defined by an incomplete information observation of a multivariate Ornstein–Uhlenbeck process X, we investigate whether we can estimate the parameters of X. We define two statistics of Y. We prove convergence properties and show how these can be used for parameter inference. Finally, numerical tests illustrate our results and indicate possible extensions and applications.

KW - Asymptotic properties of estimators

KW - Incomplete information

KW - Inference for stochastic process

KW - Stochastic graph process

U2 - 10.1007/s11203-016-9142-4

DO - 10.1007/s11203-016-9142-4

M3 - Article

AN - SCOPUS:84976295511

SN - 1387-0874

VL - 20

SP - 211

EP - 235

JO - Statistical Inference for Stochastic Processes

JF - Statistical Inference for Stochastic Processes

IS - 2

ER -

Parameter estimation of Ornstein–Uhlenbeck process generating a stochastic graph

Abstract

Keywords

Access to Document

Fingerprint

Cite this