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Isotonic estimation for grouped data

Author

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  • Woodroofe, Michael
  • Zhang, Rong

Abstract

A non-parametric estimator of a non-increasing density is found in a class of piecewise linear functions when the data consist only of counts. An EM-Algorithm for computing the estimator is developed, and the iterates in the algorithm are shown to converge to the maximum likelihood estimator. Potential applications to distance sampling models are described and illustrated with a numerical example.

Suggested Citation

  • Woodroofe, Michael & Zhang, Rong, 1999. "Isotonic estimation for grouped data," Statistics & Probability Letters, Elsevier, vol. 45(1), pages 41-47, October.
    • Handle: RePEc:eee:stapro:v:45:y:1999:i:1:p:41-47
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    Cited by:

    1. Jayanta Kumar Pal, 2009. "End‐Point Estimation for Decreasing Densities: Asymptotic Behaviour of the Penalized Likelihood Ratio," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(4), pages 764-781, December.

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