IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v49y2005i3p849-861.html

Generalised indirect classifiers

Author

Listed:
  • Peters, A.
  • Hothorn, T.
  • Lausen, B.

Abstract

No abstract is available for this item.

Suggested Citation

  • Peters, A. & Hothorn, T. & Lausen, B., 2005. "Generalised indirect classifiers," Computational Statistics & Data Analysis, Elsevier, vol. 49(3), pages 849-861, June.
  • Handle: RePEc:eee:csdana:v:49:y:2005:i:3:p:849-861
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(04)00180-X
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Vermunt, Jeroen K. & Magidson, Jay, 2003. "Latent class models for classification," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 531-537, January.
    2. J. Hand, David & Gui Li, Hua & M. Adams, Niall, 2001. "Supervised classification with structured class definitions," Computational Statistics & Data Analysis, Elsevier, vol. 36(2), pages 209-225, April.
    3. Hothorn, Torsten & Lausen, Berthold, 2003. "On the exact distribution of maximally selected rank statistics," Computational Statistics & Data Analysis, Elsevier, vol. 43(2), pages 121-137, June.
    4. Shih, Y. -S., 2004. "A note on split selection bias in classification trees," Computational Statistics & Data Analysis, Elsevier, vol. 45(3), pages 457-466, April.
    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. Adler, Werner & Lausen, Berthold, 2009. "Bootstrap estimated true and false positive rates and ROC curve," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 718-729, 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. Gerhard Tutz & Moritz Berger, 2016. "Item-focussed Trees for the Identification of Items in Differential Item Functioning," Psychometrika, Springer;The Psychometric Society, vol. 81(3), pages 727-750, September.
    2. Boulesteix, Anne-Laure & Strobl, Carolin, 2007. "Maximally selected Chi-squared statistics and non-monotonic associations: An exact approach based on two cutpoints," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6295-6306, August.
    3. Carolin Strobl & Julia Kopf & Achim Zeileis, 2015. "Rasch Trees: A New Method for Detecting Differential Item Functioning in the Rasch Model," Psychometrika, Springer;The Psychometric Society, vol. 80(2), pages 289-316, June.
    4. Strobl, Carolin & Boulesteix, Anne-Laure & Augustin, Thomas, 2007. "Unbiased split selection for classification trees based on the Gini Index," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 483-501, September.
    5. Shelley H. Liu & Yitong Chen & Jordan R. Kuiper & Emily Ho & Jessie P. Buckley & Leah Feuerstahler, 2024. "Applying Latent Variable Models to Estimate Cumulative Exposure Burden to Chemical Mixtures and Identify Latent Exposure Subgroups: A Critical Review and Future Directions," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 16(2), pages 482-502, July.
    6. Vermunt, Jeroen K., 2007. "A hierarchical mixture model for clustering three-way data sets," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5368-5376, July.
    7. Monica Pratesi & Claudio Ceccarelli & Stefano Menghinello, 2021. "Citizen-Generated Data and Official Statistics: an application to SDG indicators," Discussion Papers 2021/274, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    8. Stanislav Avsec, 2023. "Design Thinking to Envision More Sustainable Technology-Enhanced Teaching for Effective Knowledge Transfer," Sustainability, MDPI, vol. 15(2), pages 1-26, January.
    9. Mitchell D. Lingo & Wei-Lin Chen, 2023. "Righteous, Reveler, Achiever, Bored: A Latent Class Analysis of First-Year Student Involvement," Research in Higher Education, Springer;Association for Institutional Research, vol. 64(6), pages 893-932, September.
    10. Adem, Jan & Gochet, Willy, 2004. "Aggregating classifiers with mathematical programming," Computational Statistics & Data Analysis, Elsevier, vol. 47(4), pages 791-807, November.
    11. Díaz, Estrella & Martín-Consuegra, David, 2016. "A latent class segmentation analysis of airlines based on website evaluation," Journal of Air Transport Management, Elsevier, vol. 55(C), pages 20-40.
    12. Shu-Fu Kuo & Yu-Shan Shih, 2012. "Variable selection for functional density trees," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(7), pages 1387-1395, December.
    13. Maaz Gardezi & J. Gordon Arbuckle, 2019. "Spatially Representing Vulnerability to Extreme Rain Events Using Midwestern Farmers’ Objective and Perceived Attributes of Adaptive Capacity," Risk Analysis, John Wiley & Sons, vol. 39(1), pages 17-34, January.
    14. Bolger, Michelle A., 2018. "Predicting arrest probability across time: An exploration of competing risk perspectives," Journal of Criminal Justice, Elsevier, vol. 59(C), pages 92-109.
    15. Youmi Suk & Jee-Seon Kim & Hyunseung Kang, 2021. "Hybridizing Machine Learning Methods and Finite Mixture Models for Estimating Heterogeneous Treatment Effects in Latent Classes," Journal of Educational and Behavioral Statistics, , vol. 46(3), pages 323-347, June.
    16. repec:plo:pone00:0201153 is not listed on IDEAS
    17. Caspar G Chorus, 2018. "Paving the way towards superstar destinations: Models of convex demand for quality," Environment and Planning B, , vol. 45(1), pages 161-179, January.
    18. Wei-Yin Loh, 2014. "Fifty Years of Classification and Regression Trees," International Statistical Review, International Statistical Institute, vol. 82(3), pages 329-348, December.
    19. Achim Zeileis & Torsten Hothorn, 2013. "A toolbox of permutation tests for structural change," Statistical Papers, Springer, vol. 54(4), pages 931-954, November.
    20. Mevik, B.-H.Bjorn-Helge, 2004. "Assessing the performance of classifiers when classes arise from a continuum," Computational Statistics & Data Analysis, Elsevier, vol. 46(4), pages 689-705, July.
    21. Fok, Dennis & Paap, Richard & Franses, Philip Hans, 2012. "Modeling dynamic effects of promotion on interpurchase times," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3055-3069.

    More about this item

    Statistics

    Access and download statistics

    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:eee:csdana:v:49:y:2005:i:3:p:849-861. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.