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A local maximum likelihood estimator for Poisson regression

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Abstract

A local maximum likelihood estimator based on Poisson regression is presented as well as its bias, variance and asymptotic distribution. This semiparametric estimator is intended to be an alternative to the Poisson, negative binomial and zero-inflated Poisson regression models that does not depend on regularity conditions and model specification accuracy. Some simulation results are presented. The use of the local maximum likelihood procedure is illustrated on one example from the literature. This procedure is found to perform well.

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Authors and Affiliations

  1. ISEGI-UNL, Campus de Campolide, 1070-312, Lisbon, Portugal

    José António Santos

  2. Department of Mathematics, ISA, UTLisbon, Tapada da Ajuda, 1349-017, Lisbon, Portugal

    M. Manuela Neves

Authors
  1. José António Santos
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  2. M. Manuela Neves
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Corresponding author

Correspondence to José António Santos.

Additional information

This research was partially supported by Calouste Gulbenkian Foundation and PRODEP III.

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Santos, J.A., Neves, M.M. A local maximum likelihood estimator for Poisson regression. Metrika 68, 257–270 (2008). https://doi.org/10.1007/s00184-007-0156-1

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  • DOI: https://doi.org/10.1007/s00184-007-0156-1

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