Detection of multivariate outliers in business survey data with incomplete information
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DOI: 10.1007/s11634-010-0075-2
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References listed on IDEAS
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- Farnè, Matteo & Vouldis, Angelos T., 2018. "A methodology for automised outlier detection in high-dimensional datasets: an application to euro area banks' supervisory data," Working Paper Series 2171, European Central Bank.
- M. Templ & K. Hron & P. Filzmoser, 2017. "Exploratory tools for outlier detection in compositional data with structural zeros," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(4), pages 734-752, March.
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- Ju, Keyi & Su, Bin & Zhou, Dequn & Wu, Junmin & Liu, Lifan, 2016. "Macroeconomic performance of oil price shocks: Outlier evidence from nineteen major oil-related countries/regions," Energy Economics, Elsevier, vol. 60(C), pages 325-332.
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More about this item
Keywords
Multivariate outlier detection; Robust statistics; Missing values; 62G35; 62D05; 62H99;All these keywords.
JEL classification:
Statistics
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