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Resistant outlier rules and the non-Gaussian case

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  • Carling, Kenneth

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  • Carling, Kenneth, 2000. "Resistant outlier rules and the non-Gaussian case," Computational Statistics & Data Analysis, Elsevier, vol. 33(3), pages 249-258, May.
    • Handle: RePEc:eee:csdana:v:33:y:2000:i:3:p:249-258
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    References listed on IDEAS

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    1. A.C. Kimber, 1990. "Exploratory Data Analysis for Possibly Censored Data from Skewed Distributions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 39(1), pages 21-30, March.
    2. Vic Barnett, 1978. "The Study of Outliers: Purpose and Model," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 27(3), pages 242-250, November.
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    Cited by:

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    2. Fabio Galeotti & Daniel John Zizzo, 2015. "Competence versus Honesty : What Do Voters Care About ?," Working Papers 1520, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    3. Schwertman, Neil C. & Owens, Margaret Ann & Adnan, Robiah, 2004. "A simple more general boxplot method for identifying outliers," Computational Statistics & Data Analysis, Elsevier, vol. 47(1), pages 165-174, August.
    4. Jean Joël Ambagna & Sandrine Dury & Marie Claude Dop, 2019. "Estimating trends in prevalence of undernourishment: advantages of using HCES over the FAO approach in a case study from Cameroon," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 11(1), pages 93-107, February.
    5. Brandi, Giuseppe & Di Matteo, T., 2022. "Multiscaling and rough volatility: An empirical investigation," International Review of Financial Analysis, Elsevier, vol. 84(C).
    6. Fabio Galeotti & Daniel John Zizzo, 2014. "Competence versus Trustworthiness: What Do Voters Care About?," Post-Print halshs-02467510, HAL.
    7. Hubert, M. & Vandervieren, E., 2008. "An adjusted boxplot for skewed distributions," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5186-5201, August.
    8. Giuseppe Brandi & T. Di Matteo, 2022. "Multiscaling and rough volatility: an empirical investigation," Papers 2201.10466, arXiv.org.
    9. Schwertman, Neil C. & de Silva, Rapti, 2007. "Identifying outliers with sequential fences," Computational Statistics & Data Analysis, Elsevier, vol. 51(8), pages 3800-3810, May.
    10. Peter Congdon, 2017. "Quantile regression for overdispersed count data: a hierarchical method," Journal of Statistical Distributions and Applications, Springer, vol. 4(1), pages 1-19, December.
    11. Oluwasegun Taiwo Ojo & Antonio Fernández Anta & Rosa E. Lillo & Carlo Sguera, 2022. "Detecting and classifying outliers in big functional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(3), pages 725-760, September.
    12. Rand Wilcox, 2004. "Inferences Based on a Skipped Correlation Coefficient," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(2), pages 131-143.
    13. Merton S. Krause, 2018. "The scientific study of the qualities of individual human lives, rather than of their average quantities in aggregations of lives," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(3), pages 1315-1329, May.
    14. Ruijin Zhu & Weilin Guo & Xuejiao Gong, 2019. "Short-Term Photovoltaic Power Output Prediction Based on k -Fold Cross-Validation and an Ensemble Model," Energies, MDPI, vol. 12(7), pages 1-15, March.

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