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Iterative estimates for a smoothing parameter

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  • Sun, Jiayang

Abstract

A recently developed penalized non-parametric maximum likehood estimate (NPMLE), of a non-increasing density, overcomes the so-called spiking problem in the well-known NPMLE. In this paper, some iterative procedures for choosing the optimal smoothing parameter in the penalized NPMLE are considered. The iterative estimates are shown to converge and improve on adaptive estimates. Comparisons with Jackknife estimates are made. Remarks about bootstrap and kernel estimates are given.

Suggested Citation

  • Sun, Jiayang, 1995. "Iterative estimates for a smoothing parameter," Statistics & Probability Letters, Elsevier, vol. 24(4), pages 347-356, September.
    • Handle: RePEc:eee:stapro:v:24:y:1995:i:4:p:347-356
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