On the asymptotic mean integrated squared error of a kernel density estimator for dependent data

Jan Mielniczuk

Statistics & Probability Letters, 1997, vol. 34, issue 1, 53-58

Abstract: Hall and Hart (1990) proved that the mean integrated squared error (MISE) of a marginal kernel density estimator from an infinite moving average process X1, X2, ... may be decomposed into the sum of MISE of the same kernel estimator for a random sample of the same size and a term proportional to the variance of the sample mean. Extending this, we show here that the phenomenon is rather general: the same result continues to hold if dependence is quantified in terms of the behaviour of a remainder term in a natural decomposition of the densities of (X1, X1+i), I = 1, 2, ....

Keywords: Kernel; estimator; Long-range; dependence; Mean; integrated; square; error (search for similar items in EconPapers)
Date: 1997
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Citations: View citations in EconPapers (3)

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