On parametric estimation of population abundance for line transect sampling

Shunpu Zhang

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Despite recent advances in nonparametric methods for estimating animal abundance, parametric methods are still used widely among biometricians due to their simplicity. In this paper, we propose an optimal shrinkage-type estimator and an empirical Bayes estimator for estimating animal density from line transect sampling data. The performances of the proposed estimators are compared with those of the maximum likelihood estimator and a bias-corrected maximum likelihood estimator both theoretically and numerically. Simulation results show that the optimal shrinkage-type estimator works the best if the detection function has a very thin tail (for example, the half normal detection function), while the maximum likelihood estimator is the best estimator if the detection function has relatively thick tail (for example, the polynomial detection function).

Original languageEnglish (US)
Pages (from-to)79-92
Number of pages14
JournalEnvironmental and Ecological Statistics
Issue number1
StatePublished - Mar 2011


  • Bayes estimator
  • Empirical Bayes estimator
  • Line transect sampling
  • Maximum likelihood estimator
  • Shrinkage estimator

ASJC Scopus subject areas

  • Statistics and Probability
  • Environmental Science(all)
  • Statistics, Probability and Uncertainty


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