IDEAS home Printed from https://ideas.repec.org/a/spr/advdac/v6y2012i1p29-47.html
   My bibliography  Save this article

Exploring incomplete data using visualization techniques

Author

Listed:
  • Matthias Templ
  • Andreas Alfons
  • Peter Filzmoser

Abstract

No abstract is available for this item.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11634-011-0102-y
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s11634-011-0102-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Kowarik, Alexander & Templ, Matthias, 2016. "Imputation with the R Package VIM," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i07).
    3. 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.
    4. 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.
    5. 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).
    6. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pilhöfer, Alexander & Unwin, Antony, 2013. "New Approaches in Visualization of Categorical Data: R Package extracat," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 53(i07).
    2. 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.
    3. Nikola Štefelová & Andreas Alfons & Javier Palarea-Albaladejo & Peter Filzmoser & Karel Hron, 2021. "Robust regression with compositional covariates including cellwise outliers," 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. 15(4), pages 869-909, December.
    4. Giovanni C. Porzio & Giancarlo Ragozini & Domenico Vistocco, 2008. "On the use of archetypes as benchmarks," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 24(5), pages 419-437, September.
    5. Marco Riani & Anthony C. Atkinson & Aldo Corbellini, 2023. "Automatic robust Box–Cox and extended Yeo–Johnson transformations in regression," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(1), pages 75-102, March.
    6. Henry Webel & Lili Niu & Annelaura Bach Nielsen & Marie Locard-Paulet & Matthias Mann & Lars Juhl Jensen & Simon Rasmussen, 2024. "Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    7. Ligges, Uwe & Maechler, Martin, 2003. "scatterplot3d - An R Package for Visualizing Multivariate Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 8(i11).
    8. Simon Urbanek, 2011. "iPlots eXtreme: next-generation interactive graphics design and implementation of modern interactive graphics," Computational Statistics, Springer, vol. 26(3), pages 381-393, September.
    9. Torti, Francesca & Corbellini, Aldo & Atkinson, Anthony C., 2021. "fsdaSAS: a package for robust regression for very large datasets including the batch forward search," LSE Research Online Documents on Economics 109895, London School of Economics and Political Science, LSE Library.
    10. Yee, Thomas W., 2010. "The VGAM Package for Categorical Data Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 32(i10).
    11. Stanislav Katina & Liberty Vittert & Adrian W. Bowman, 2021. "Functional data analysis and visualisation of three‐dimensional surface shape," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(3), pages 691-713, June.
    12. repec:jss:jstsof:32:i01 is not listed on IDEAS
    13. repec:jss:jstsof:08:i11 is not listed on IDEAS
    14. Nadia Solaro & Alessandro Barbiero & Giancarlo Manzi & Pier Alda Ferrari, 2017. "A sequential distance-based approach for imputing missing data: Forward Imputation," 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. 11(2), pages 395-414, June.
    15. Jonas Schöley, 2021. "The centered ternary balance scheme: A technique to visualize surfaces of unbalanced three-part compositions," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 44(19), pages 443-458.
    16. Cheng, Xiaoyue & Cook, Dianne & Hofmann, Heike, 2015. "Visually Exploring Missing Values in Multivariable Data Using a Graphical User Interface," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 68(i06).
    17. Tutz, Gerhard & Ramzan, Shahla, 2015. "Improved methods for the imputation of missing data by nearest neighbor methods," Computational Statistics & Data Analysis, Elsevier, vol. 90(C), pages 84-99.
    18. John W. Emerson, 2008. "Interactive and Dynamic Graphics for Data Analysis: With R and GGobi by COOK, D. and SWAYNE, D," Biometrics, The International Biometric Society, vol. 64(4), pages 1301-1303, December.
    19. Takahiro Yoshida & Morito Tsutsumi, 2018. "On the effects of spatial relationships in spatial compositional multivariate models," Letters in Spatial and Resource Sciences, Springer, vol. 11(1), pages 57-70, March.
    20. Michael Lawrence & Hadley Wickham & Dianne Cook & Heike Hofmann & Deborah Swayne, 2009. "Extending the GGobi pipeline from R," Computational Statistics, Springer, vol. 24(2), pages 195-205, May.
    21. 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.
    22. Huiwen Wang & Shan Lu & Yide Liu, 2022. "Missing data imputation in PLS-SEM," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4777-4795, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:advdac:v:6:y:2012:i:1:p:29-47. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.