Robust Likelihood Cross-Validation for Kernel Density Estimation

Ximing Wu

Journal of Business & Economic Statistics, 2019, vol. 37, issue 4, 761-770

Abstract: Likelihood cross-validation for kernel density estimation is known to be sensitive to extreme observations and heavy-tailed distributions. We propose a robust likelihood-based cross-validation method to select bandwidths in multivariate density estimations. We derive this bandwidth selector within the framework of robust maximum likelihood estimation. This method establishes a smooth transition from likelihood cross-validation for nonextreme observations to least squares cross-validation for extreme observations, thereby combining the efficiency of likelihood cross-validation and the robustness of least-squares cross-validation. We also suggest a simple rule to select the transition threshold. We demonstrate the finite sample performance and practical usefulness of the proposed method via Monte Carlo simulations and a real data application on Chinese air pollution.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlbes:v:37:y:2019:i:4:p:761-770

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DOI: 10.1080/07350015.2018.1424633

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