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Exploring incomplete data using visualization techniques

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  • Matthias Templ
  • Andreas Alfons
  • Peter Filzmoser

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  • Matthias Templ & Andreas Alfons & Peter Filzmoser, 2012. "Exploring incomplete data using visualization techniques," 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. 6(1), pages 29-47, April.
    • Handle: RePEc:spr:advdac:v:6:y:2012:i:1:p:29-47
    DOI: 10.1007/s11634-011-0102-y
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    References listed on IDEAS

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    1. Domenico Perrotta & Marco Riani & Francesca Torti, 2009. "New robust dynamic plots for regression mixture detection," 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. 3(3), pages 263-279, December.
    2. Zeileis, Achim & Hornik, Kurt & Murrell, Paul, 2009. "Escaping RGBland: Selecting colors for statistical graphics," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3259-3270, July.
    3. Hron, K. & Templ, M. & Filzmoser, P., 2010. "Imputation of missing values for compositional data using classical and robust methods," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3095-3107, December.
    4. Meyer, David & Zeileis, Achim & Hornik, Kurt, 2006. "The Strucplot Framework: Visualizing Multi-way Contingency Tables with vcd," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 17(i03).
    5. Theus, Martin, 2002. "Interactive Data Visualization using Mondrian," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 7(i11).
    6. Hofmann, Heike, 2003. "Constructing and reading mosaicplots," Computational Statistics & Data Analysis, Elsevier, vol. 43(4), pages 565-580, August.
    7. Swayne, Deborah F. & Lang, Duncan Temple & Buja, Andreas & Cook, Dianne, 2003. "GGobi: evolving from XGobi into an extensible framework for interactive data visualization," Computational Statistics & Data Analysis, Elsevier, vol. 43(4), pages 423-444, August.
    8. Valentin Todorov & Matthias Templ & Peter Filzmoser, 2011. "Detection of multivariate outliers in business survey data with incomplete information," 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. 5(1), pages 37-56, April.
    9. Julie Josse & Jérôme Pagès & François Husson, 2011. "Multiple imputation in principal component analysis," 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. 5(3), pages 231-246, October.
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    Cited by:

    1. Ahrum Son & Hyunsoo Kim & Jolene K. Diedrich & Casimir Bamberger & Daniel B. McClatchy & Stuart A. Lipton & John R. Yates, 2024. "Using in vivo intact structure for system-wide quantitative analysis of changes in proteins," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    2. Juana Sanchez & Sydney Noelle Kahmann, 2017. "R&D, Attrition and Multiple Imputation in BRDIS," Working Papers 17-13, Center for Economic Studies, U.S. Census Bureau.
    3. Kowarik, Alexander & Templ, Matthias, 2016. "Imputation with the R Package VIM," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i07).
    4. Elena Catanese, 2016. "Data Editing for Complex Surveys in Presence Of Administrative Data: An Application to Fss 2013 Livestock Survey Data Based on The Joint Sequential Use Of Different R Packages," Romanian Statistical Review, Romanian Statistical Review, vol. 64(2), pages 101-117, June.
    5. 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.
    6. Alfons, Andreas & Templ, Matthias, 2013. "Estimation of Social Exclusion Indicators from Complex Surveys: The R Package laeken," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 54(i15).
    7. Matthias Templ & Alexander Kowarik & Bernhard Meindl, 2014. "Development and Current Practice in Using R at Statistics Austria," Romanian Statistical Review, Romanian Statistical Review, vol. 62(2), pages 173-184, June.

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