Abstract
The problem of estimating the marginal density of a linear process by kernel methods is considered. Under general conditions, kernel density estimators are shown to be asymptotically normal. Their limiting covariance matrix is computed. We also find the optimal bandwidth in the sense that it asymptotically minimizes the mean square error of the estimators. The assumptions involved are easily verifiable.
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Supported in part by NSF grant DMS-9403718.
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Hallin, M., Tran, L.T. Kernel density estimation for linear processes: Asymptotic normality and optimal bandwidth derivation. Ann Inst Stat Math 48, 429–449 (1996). https://doi.org/10.1007/BF00050847
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DOI: https://doi.org/10.1007/BF00050847