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• W2963124325 abstract "Abstract We research an adaptive maximum‐likelihood–type estimation for an ergodic diffusion process where the observation is contaminated by noise. This methodology leads to the asymptotic independence of the estimators for the variance of observation noise, the diffusion parameter, and the drift one of the latent diffusion process. Moreover, it can lessen the computational burden compared to simultaneous maximum likelihood–type estimation. In addition to adaptive estimation, we propose a test to see if noise exists or not and analyze real data as the example such that the data contain observation noise with statistical significance." @default.
• W2963124325 created "2019-07-30" @default.
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• W2963124325 date "2018-12-05" @default.
• W2963124325 modified "2023-10-16" @default.
• W2963124325 title "Inference for ergodic diffusions plus noise" @default.
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• W2963124325 doi "https://doi.org/10.1111/sjos.12360" @default.
• W2963124325 hasPublicationYear "2018" @default.
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