Minimum density power divergence estimator for diffusion processes

Sangyeol Lee; Junmo Song

doi:10.1007/s10463-012-0366-9

Minimum density power divergence estimator for diffusion processes

Sangyeol Lee, Junmo Song

Seoul National University

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

6 Scopus citations

Abstract

In this paper, we consider the robust estimation for a certain class of diffusion processes including the Ornstein-Uhlenbeck process based on discrete observations. As a robust estimator, we consider the minimum density power divergence estimator (MDPDE) proposed by Basu et al. (Biometrika 85:549-559, 1998). It is shown that the MDPDE is consistent and asymptotically normal. A simulation study demonstrates the strong robustness of the MDPDE.

Original language	English
Pages (from-to)	213-236
Number of pages	24
Journal	Annals of the Institute of Statistical Mathematics
Volume	65
Issue number	2
DOIs	https://doi.org/10.1007/s10463-012-0366-9
State	Published - Apr 2013

Keywords

Diffusion processes
Discretely observed sample
Minimum density power divergence estimator
Robustness
The Ornstein-Uhlenbeck process

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10.1007/s10463-012-0366-9

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abstract = "In this paper, we consider the robust estimation for a certain class of diffusion processes including the Ornstein-Uhlenbeck process based on discrete observations. As a robust estimator, we consider the minimum density power divergence estimator (MDPDE) proposed by Basu et al. (Biometrika 85:549-559, 1998). It is shown that the MDPDE is consistent and asymptotically normal. A simulation study demonstrates the strong robustness of the MDPDE.",

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AB - In this paper, we consider the robust estimation for a certain class of diffusion processes including the Ornstein-Uhlenbeck process based on discrete observations. As a robust estimator, we consider the minimum density power divergence estimator (MDPDE) proposed by Basu et al. (Biometrika 85:549-559, 1998). It is shown that the MDPDE is consistent and asymptotically normal. A simulation study demonstrates the strong robustness of the MDPDE.

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KW - Discretely observed sample

KW - Minimum density power divergence estimator

KW - Robustness

KW - The Ornstein-Uhlenbeck process

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JF - Annals of the Institute of Statistical Mathematics

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Minimum density power divergence estimator for diffusion processes

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