Two generalized nonparametric methods for estimating like densities

Zongyuan Shang and Alan Ker ()
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Zongyuan Shang: University of Guelph
Alan Ker: University of Guelph

Computational Statistics, 2021, vol. 36, issue 1, No 5, 113-126

Abstract: Abstract This article presents two generalized nonparametric methods for estimating multiple, possibly like, densities. The first generalization contains the Nadaraya–Watson estimator, the Jones et al. (Biometrika 82(2):327–338, 1995) bias reduction estimator, and Ker (Stat Probab Lett 117:23–30, 2016) possibly similar estimator as special cases. The second generalization contains the Nadaraya–Watson estimator, Ker (2016) possibly similar estimator, and the conditional density estimator of Hall et al. (J Am Stat Assoc 99(468):1015–1026, 2004) as special cases. The generalizations do not require knowledge of the form or extent of likeness between the unknown densities; an attractive feature in empirical applications. Numerical simulations demonstrate that the two proposed generalizations lead to significant efficiency gains.

Keywords: Multiple density estimation; Combined estimator; Kernel smoothing (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s00180-020-01007-w

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