{
"@context": "http://schema.org",
"@type": "ScholarlyArticle",
"@id": "https://doi.org/10.5281/zenodo.1100604",
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"value": "https://zenodo.org/record/1100605"
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"url": "https://zenodo.org/record/1100604",
"additionalType": "Journal article",
"name": "Alternative Robust Estimators For The Shape Parameters Of The Burr Xii Distribution",
"author": [
{
"name": "F. Z. Doğru"
},
{
"name": "O. Arslan"
}
],
"description": "In general, classical methods such as maximum
\nlikelihood (ML) and least squares (LS) estimation methods are used
\nto estimate the shape parameters of the Burr XII distribution.
\nHowever, these estimators are very sensitive to the outliers. To
\novercome this problem we propose alternative robust estimators
\nbased on the M-estimation method for the shape parameters of the
\nBurr XII distribution. We provide a small simulation study and a real
\ndata example to illustrate the performance of the proposed estimators
\nover the ML and the LS estimators. The simulation results show that
\nthe proposed robust estimators generally outperform the classical
\nestimators in terms of bias and root mean square errors when there
\nare outliers in data.",
"license": [
"https://creativecommons.org/licenses/by/4.0",
"info:eu-repo/semantics/openAccess"
],
"keywords": "Burr XII distribution, robust estimator, M-estimator, maximum likelihood, least squares.",
"inLanguage": "en",
"datePublished": "2015-04-02",
"schemaVersion": "http://datacite.org/schema/kernel-4",
"publisher": {
"@type": "Organization",
"name": "Zenodo"
},
"provider": {
"@type": "Organization",
"name": "datacite"
}
}