A Monte Carlo simulation study of a robust estimator used in the inference of a contaminated binomial model - doi: 10.4025/actascitechnol.v32i3.4145

Authors

  • Augusto Maciel da Silva Programa de Pós-Graduação em Estatí­stica e Experimentação Agropecuária, Universidade Federal de Lavras, Lavras Author
  • Marcelo í‚ngelo Cirillo Programa de Pós-Graduação em Estatí­stica e Experimentação Agropecuária, Universidade Federal de Lavras, Lavras Author

DOI:

https://doi.org/10.4025/actascitechnol.v32i3.4145

Keywords:

binomial distribution, contaminated binomial, Monte Carlo, robustness

Abstract

The statistical inference in binomial population is subject to gross errors of estimate, as the samples are not identically distributed. Due to this problem, this work aims to determine which is the best affinity constant (c1) that provides the best performance in the estimator, belonging to the class of E-estimators. With that purpose, the methodology used in this work was applied considering the Monte Carlo simulation method, in which different configurations described by combination of parametric values, levels of contamination and sample sizes were appraised. It was concluded that for the high probability of contamination (γ = 0.40), c1 = 0.1 is recommended in cases with large samples (n = 50 and n = 80).

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Published

2010-11-09

Issue

Vol. 32 No. 3 (2010)

Section

Statistics

License

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The copyrights belong exclusively to the authors. Published content is licensed under Creative Commons Attribution 4.0 (CC BY 4.0) guidelines, which allows sharing (copy and distribution of the material in any medium or format) and adaptation (remix, transform, and build upon the material) for any purpose, even commercially, under the terms of attribution.

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How to Cite

Silva, A. M. da, & Cirillo, M. í‚ngelo. (2010). A Monte Carlo simulation study of a robust estimator used in the inference of a contaminated binomial model - doi: 10.4025/actascitechnol.v32i3.4145. Acta Scientiarum. Technology, 32(3), 303-307. https://doi.org/10.4025/actascitechnol.v32i3.4145

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