<b>A Monte Carlo simulation study of a robust estimator used in the inference of a contaminated binomial model</b> - doi: 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).Downloads
Download data is not yet available.
Published
2010-11-09
How to Cite
Silva, A. M. da, & Cirillo, M. Ângelo. (2010). <b>A Monte Carlo simulation study of a robust estimator used in the inference of a contaminated binomial model</b> - doi: 10.4025/actascitechnol.v32i3.4145. Acta Scientiarum. Technology, 32(3), 303-307. https://doi.org/10.4025/actascitechnol.v32i3.4145
Issue
Section
Statistics
DECLARATION OF ORIGINALITY AND COPYRIGHTS
I Declare that current article is original and has not been submitted for publication, in part or in whole, to any other national or international journal.
The copyrights belong exclusively to the authors. Published content is licensed under Creative Commons Attribution 3.0 (CC BY 3.0) 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.
Read this link for further information on how to use CC BY 3.0 properly.
0.8
2019CiteScore
36th percentile
Powered by
0.8
2019CiteScore
36th percentile
Powered by