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A Bayesian nonparametric estimator based on left censored data

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Summary

This paper introduces a Bayesian nonparametric estimator for an unknown distribution function based on left censored observations.

Hjort (1990)/Lo (1993) introduced Bayesian nonparametric estimators derived from beta/beta-neutral processes which allow for right censoring. These processes are taken as priors from the class ofneutral to the right processes (Doksum, 1974). The Kaplan-Meier nonparametric product limit estimator can be obtained from these Bayesian nonparametric estimators in the limiting case of a vague prior.

The present paper introduces what can be seen as the correspondingleft beta/beta-neutral process prior which allow for left censoring. The Bayesian nonparametyric estimator is obtained as in the corresponding product limit estimator based on left censored data.

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Authors and Affiliations

  1. Imperial College, London, UK

    Stephen Walker

  2. Dipartimento di Economica Politica e Metodi Quantitativi, Università di Pavia, Via S. Felice, 27100, Pavia, Italy

    Pietro Muliere

Authors
  1. Stephen Walker
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  2. Pietro Muliere
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Correspondence to Pietro Muliere.

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Walker, S., Muliere, P. A Bayesian nonparametric estimator based on left censored data. J. It. Statist. Soc. 5, 285–295 (1996). https://doi.org/10.1007/BF02589177

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  • DOI: https://doi.org/10.1007/BF02589177

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