Asymptotic Normality for Density Kernel Estimators in Discrete and Continuous Time

Denis Bosq, Florence Merlevède and Magda Peligrad

Journal of Multivariate Analysis, 1999, vol. 68, issue 1, 78-95

Abstract: 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.

Keywords: central; limit; theorem; strongly; mixing; sequence; triangular; array; Kernel; estimator; continuous; and; discrete; time; processes (search for similar items in EconPapers)
Date: 1999
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Citations: View citations in EconPapers (11)

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