High breakdown point robust estimators with missing data
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2017/11/20
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Indian UGC (journal)
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Refrences18
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Citations1
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Florencia Statti Instituto de Cálculo, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, CONICET, Argentina
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Mariela Sued Instituto de Cálculo, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, CONICET, Argentina
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Victor J. Yohai Departamento de Matemática, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires and CONICET, Argentina
Cite
Statti, Florencia, et al. “High Breakdown Point Robust Estimators With Missing Data”. Communications in Statistics - Theory and Methods, vol. 47, no. 21, 2017, pp. 5145-62, https://doi.org/10.1080/03610926.2017.1388396.
Statti, F., Sued, M., & Yohai, V. J. (2017). High breakdown point robust estimators with missing data. Communications in Statistics - Theory and Methods, 47(21), 5145-5162. https://doi.org/10.1080/03610926.2017.1388396
Statti, Florencia, Mariela Sued, and Victor J. Yohai. “High Breakdown Point Robust Estimators With Missing Data”. Communications in Statistics - Theory and Methods 47, no. 21 (2017): 5145-62. https://doi.org/10.1080/03610926.2017.1388396.
Statti F, Sued M, Yohai VJ. High breakdown point robust estimators with missing data. Communications in Statistics - Theory and Methods. 2017;47(21):5145-62.
Journal Categories
Science
Mathematics
Science
Mathematics
Probabilities
Mathematical statistics
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Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Title | 1986 | |||
| Annals of Mathematical Statistics | 1964 | |||
| Annals of Mathematical Statistics | 1983 | |||
Causal Inference on Quantiles with an Obstetric Application | Biometrics |
| 26 | 2011 |
| Imputation approaches for potential outcomes in causal inference | International Journal of Epidemiology |
| 34 | 2015 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Robust doubly protected estimators for quantiles with missing data | TEST |
| 2019 |
Citations Analysis
| Category | Category Repetition |
|---|---|
| Science: Mathematics: Probabilities. Mathematical statistics | 1 |
| Science: Mathematics | 1 |
The category
Science: Mathematics: Probabilities. Mathematical statistics 1
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Robust doubly protected estimators for quantiles with missing data and was published in 2019. The most recent citation comes from a 2019 study titled Robust doubly protected estimators for quantiles with missing data. This article reached its peak citation in 2019, with 1 citations. It has been cited in 1 different journals. Among related journals, the TEST cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.