IDEAS home Printed from https://ideas.repec.org/a/spr/jclass/v29y2012i1p76-90.html
   My bibliography  Save this article

A Property of the CHAID Partitioning Method for Dichotomous Randomized Response Data and Categorical Predictors

Author

Listed:
  • Pier Perri
  • Peter Heijden

Abstract

No abstract is available for this item.

Suggested Citation

  • Pier Perri & Peter Heijden, 2012. "A Property of the CHAID Partitioning Method for Dichotomous Randomized Response Data and Categorical Predictors," Journal of Classification, Springer;The Classification Society, vol. 29(1), pages 76-90, April.
    • Handle: RePEc:spr:jclass:v:29:y:2012:i:1:p:76-90
    DOI: 10.1007/s00357-011-9094-8
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00357-011-9094-8
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s00357-011-9094-8?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. Siciliano, Roberta & Mola, Francesco, 2000. "Multivariate data analysis and modeling through classification and regression trees," Computational Statistics & Data Analysis, Elsevier, vol. 32(3-4), pages 285-301, January.
    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. Lucio Barabesi & Giancarlo Diana & Pier Perri, 2015. "Gini index estimation in randomized response surveys," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 99(1), pages 45-62, January.

    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. Mariangela Sciandra & Antonella Plaia & Vincenza Capursi, 2017. "Classification trees for multivariate ordinal response: an application to Student Evaluation Teaching," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 641-655, March.
    2. Claudio Conversano & Francesco Mola & Roberta Siciliano, 2001. "Partitioning Algorithms and Combined Model Integration for Data Mining," Computational Statistics, Springer, vol. 16(3), pages 323-339, September.
    3. Piccarreta, Raffaella, 2010. "Binary trees for dissimilarity data," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1516-1524, June.
    4. George Petrakos & Claudio Conversano & Gregory Farmakis & Francesco Mola & Roberta Siciliano & Photis Stavropoulos, 2004. "New ways of specifying data edits," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(2), pages 249-274, May.
    5. Noh, Hyun Gon & Song, Moon Sup & Park, Sung Hyun, 2004. "An unbiased method for constructing multilabel classification trees," Computational Statistics & Data Analysis, Elsevier, vol. 47(1), pages 149-164, August.
    6. Claudio Conversano & Roberta Siciliano, 2009. "Incremental Tree-Based Missing Data Imputation with Lexicographic Ordering," Journal of Classification, Springer;The Classification Society, vol. 26(3), pages 361-379, December.
    7. Amodio, S. & D’Ambrosio, A. & Siciliano, R., 2016. "Accurate algorithms for identifying the median ranking when dealing with weak and partial rankings under the Kemeny axiomatic approach," European Journal of Operational Research, Elsevier, vol. 249(2), pages 667-676.
    8. Roberta Siciliano & Antonio D’Ambrosio & Massimo Aria & Sonia Amodio, 2017. "Analysis of Web Visit Histories, Part II: Predicting Navigation by Nested STUMP Regression Trees," Journal of Classification, Springer;The Classification Society, vol. 34(3), pages 473-493, October.
    9. Lee, Tzu-Haw & Shih, Yu-Shan, 2006. "Unbiased variable selection for classification trees with multivariate responses," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 659-667, November.
    10. Antonio D’Ambrosio & Willem J. Heiser, 2016. "A Recursive Partitioning Method for the Prediction of Preference Rankings Based Upon Kemeny Distances," Psychometrika, Springer;The Psychometric Society, vol. 81(3), pages 774-794, September.
    11. Cappelli, Carmela & Mola, Francesco & Siciliano, Roberta, 2002. "A statistical approach to growing a reliable honest tree," Computational Statistics & Data Analysis, Elsevier, vol. 38(3), pages 285-299, January.
    12. Dine, Abdessamad & Larocque, Denis & Bellavance, François, 2009. "Multivariate trees for mixed outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3795-3804, September.
    13. Schmid, Lena & Gerharz, Alexander & Groll, Andreas & Pauly, Markus, 2023. "Tree-based ensembles for multi-output regression: Comparing multivariate approaches with separate univariate ones," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).

    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:jclass:v:29:y:2012:i:1:p:76-90. 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.