The problem of nonparametric invariant density function estimation of an ergodic di usion process is considered. The local asymptotic minimax lower bound on the risk of all the estimators is established. The asymptotic risk considered measures the distance between the estimators and the density that
On unbiased density estimation for ergodic diffusion
β Scribed by Yu.A. Kutoyants
- Publisher
- Elsevier Science
- Year
- 1997
- Tongue
- English
- Weight
- 316 KB
- Volume
- 34
- Category
- Article
- ISSN
- 0167-7152
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β¦ Synopsis
Two classes of unbiased estimators of the density function of ergodic distribution for the diffusion process of observations are proposed. The estimators are square-root consistent and asymptotically normal. This curious situation is entirely different from the case of discrete-time models (Davis 1977) where the unbiased estimator rarely exists and usually the estimators are not square-root consistent.
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In the problems of invariant distribution and density estimation of an ergodic di usion process, the asymptotic variances of many estimators can be represented as some mathematical expectations with respect to the invariant law. Therefore, the construction of the conΓΏdence intervals requires the est
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