In order to construct confidence sets for a marginal density f of a strictly stationary continuous time process observed over the time interval [0, T ], it is necessary to have at one's disposal a Central Limit Theorem for the kernel density estimator f T . In this paper we address the question of n
Asymptotic Normality for Density Kernel Estimators in Discrete and Continuous Time
✍ Scribed by Denis Bosq; Florence Merlevède; Magda Peligrad
- Publisher
- Elsevier Science
- Year
- 1999
- Tongue
- English
- Weight
- 153 KB
- Volume
- 68
- Category
- Article
- ISSN
- 0047-259X
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✦ Synopsis
In this paper, we build a central limit theorem for triangular arrays of sequences which satisfy a mild mixing condition. This result allows us to study asymptotic normality of density kernel estimators for some classes of continuous and discrete time processes.
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