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A Bias Correction for Cross‐validation Bandwidth Selection when a Kernel Estimate is Based on Dependent Data

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  • Martin Skold

Abstract

Least‐squares cross‐validation (LSCV) bandwidth selection for kernel density estimation has been shown to underestimate the optimal bandwidth if data are positively correlated. We calculate the asymptotic bias for the LSCV criterion under a continuous‐time model and apply it as a correction term to discrete‐time data that can be modeled as a smooth continuous‐time process sampled at a high rate.

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  • Martin Skold, 2001. "A Bias Correction for Cross‐validation Bandwidth Selection when a Kernel Estimate is Based on Dependent Data," Journal of Time Series Analysis, Wiley Blackwell, vol. 22(4), pages 493-503, July.
    • Handle: RePEc:bla:jtsera:v:22:y:2001:i:4:p:493-503
    DOI: 10.1111/1467-9892.00237
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