Asymptotic Normality for Density Kernel Estimators in Discrete and Continuous Time
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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Volume (Year): 68 (1999)
Issue (Month): 1 (January)
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References listed on IDEAS
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- Kutoyants, Yu. A., 1997. "Some problems of nonparametric estimation by observations of ergodic diffusion process," Statistics & Probability Letters, Elsevier, vol. 32(3), pages 311-320, March.
- Bradley, Richard C., 1983. "Asymptotic normality of some kernel-type estimators of probability density," Statistics & Probability Letters, Elsevier, vol. 1(6), pages 295-300, October.
- Castellana, J. V. & Leadbetter, M. R., 1986. "On smoothed probability density estimation for stationary processes," Stochastic Processes and their Applications, Elsevier, vol. 21(2), pages 179-193, February.
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