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Asymptotic Properties for Methods Combining the Minimum Hellinger Distance Estimate and the Bayesian Nonparametric Density Estimate
In frequentist inference, minimizing the Hellinger distance between a kernel density estimate and a parametric family produces estimators that are both robust to outliers and statistically efficient when the parametric family contains the data-generating distribution. This paper seeks to extend thes...
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| Vydáno v: | Entropy (Basel) |
|---|---|
| Hlavní autoři: | , |
| Médium: | Artigo |
| Jazyk: | Inglês |
| Vydáno: |
MDPI
2018
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| Témata: | |
| On-line přístup: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7512539/ https://ncbi.nlm.nih.gov/pubmed/33266679 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/e20120955 |
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