IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v53y2009i8p3103-3106.html
   My bibliography  Save this article

Special issue on correspondence analysis and related methods

Author

Listed:
  • Blasius, J.
  • Greenacre, M.
  • Groenen, P.J.F.
  • van de Velden, M.

Abstract

No abstract is available for this item.

Suggested Citation

  • Blasius, J. & Greenacre, M. & Groenen, P.J.F. & van de Velden, M., 2009. "Special issue on correspondence analysis and related methods," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3103-3106, June.
  • Handle: RePEc:eee:csdana:v:53:y:2009:i:8:p:3103-3106
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(08)00552-5
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    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. Park, Mira & Lee, Jae Won & Kim, Choongrak, 2007. "Correspondence analysis approach for finding allele associations in population genetic study," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 3145-3155, March.
    2. DeSarbo, Wayne S. & Selin Atalay, A. & Blanchard, Simon J., 2009. "A three-way clusterwise multidimensional unfolding procedure for the spatial representation of context dependent preferences," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3217-3230, June.
    3. Lombardo, R. & Beh, E.J. & D'Ambra, L., 2007. "Non-symmetric correspondence analysis with ordinal variables using orthogonal polynomials," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 566-577, September.
    4. Vichi, Maurizio & Saporta, Gilbert, 2009. "Clustering and disjoint principal component analysis," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3194-3208, June.
    5. van de Velden, Michel & Groenen, Patrick J.F. & Poblome, Jeroen, 2009. "Seriation by constrained correspondence analysis: A simulation study," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3129-3138, June.
    6. Greenacre, Michael, 2009. "Power transformations in correspondence analysis," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3107-3116, June.
    7. de Rooij, Mark, 2009. "Trend vector models for the analysis of change in continuous time for multiple groups," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3209-3216, June.
    8. Blasius, Jörg & Eilers, Paul H.C. & Gower, John, 2009. "Better biplots," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3145-3158, June.
    9. Vera, J. Fernando & Macas, Rodrigo & Heiser, Willem J., 2009. "A dual latent class unfolding model for two-way two-mode preference rating data," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3231-3244, June.
    10. ter Braak, Cajo J.F. & Kourmpetis, Yiannis & Kiers, Henk A.L. & Bink, Marco C.A.M., 2009. "Approximating a similarity matrix by a latent class model: A reappraisal of additive fuzzy clustering," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3183-3193, June.
    11. Zárraga, A. & Goitisolo, B., 2009. "Simultaneous analysis and multiple factor analysis for contingency tables: Two methods for the joint study of contingency tables," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3171-3182, June.
    12. Warrens, Matthijs J. & Heiser, Willem J., 2009. "Diagnostics for regression dependence in tables re-ordered by the dominant correspondence analysis solution," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3139-3144, June.
    13. M. O. Hill, 1974. "Correspondence Analysis: A Neglected Multivariate Method," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 23(3), pages 340-354, November.
    14. Polak, Marike & Heiser, Willem J. & de Rooij, Mark, 2009. "Two types of single-peaked data: Correspondence analysis as an alternative to principal component analysis," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3117-3128, June.
    15. Takane, Yoshio & Jung, Sunho, 2009. "Regularized nonsymmetric correspondence analysis," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3159-3170, June.
    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. Kenneth David Strang, 2012. "Man versus math: Behaviorist exploration of post-crisis non-banking asset management," Journal of Asset Management, Palgrave Macmillan, vol. 13(5), pages 348-367, October.
    2. K. Fernández-Aguirre & M. Garín-Martín & J. Modroño-Herrán, 2014. "Visual displays: analytical study and applications to graphs and real data," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(4), pages 2209-2224, July.

    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. Hui, Francis K.C., 2017. "Model-based simultaneous clustering and ordination of multivariate abundance data in ecology," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 1-10.
    2. Antonello D’Ambra & Pietro Amenta, 2011. "Correspondence Analysis with Linear Constraints of Ordinal Cross-Classifications," Journal of Classification, Springer;The Classification Society, vol. 28(1), pages 70-92, April.
    3. D'Ambra, Luigi & Amenta, Pietro & D'Ambra, Antonello & de Tibeiro, Jules S., 2021. "A study of the family service expenditures and the socio-demographic characteristics via fixed marginals correspondence analysis," Socio-Economic Planning Sciences, Elsevier, vol. 73(C).
    4. Alfonso Iodice D’Enza & Francesco Palumbo, 2013. "Iterative factor clustering of binary data," Computational Statistics, Springer, vol. 28(2), pages 789-807, April.
    5. Daniel M. Ringel & Bernd Skiera, 2016. "Visualizing Asymmetric Competition Among More Than 1,000 Products Using Big Search Data," Marketing Science, INFORMS, vol. 35(3), pages 511-534, May.
    6. la Grange, Anthony & le Roux, Niël & Gardner-Lubbe, Sugnet, 2009. "BiplotGUI: Interactive Biplots in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 30(i12).
    7. Kohei Adachi & Nickolay T. Trendafilov, 2018. "Sparsest factor analysis for clustering variables: a matrix decomposition approach," 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. 12(3), pages 559-585, September.
    8. Delimiro Visbal-Cadavid & Mónica Martínez-Gómez & Rolando Escorcia-Caballero, 2020. "Exploring University Performance through Multiple Factor Analysis: A Case Study," Sustainability, MDPI, vol. 12(3), pages 1-24, January.
    9. Yannis Yatracos, 2013. "Detecting Clusters in the Data from Variance Decompositions of Its Projections," Journal of Classification, Springer;The Classification Society, vol. 30(1), pages 30-55, April.
    10. Diane Duffy & adolfo Quiroz, 1991. "A permutation-based algorithm for block clustering," Journal of Classification, Springer;The Classification Society, vol. 8(1), pages 65-91, January.
    11. Dawn Iacobucci & Doug Grisaffe, 2018. "Perceptual maps via enhanced correspondence analysis: representing confidence regions to clarify brand positions," Journal of Marketing Analytics, Palgrave Macmillan, vol. 6(3), pages 72-83, September.
    12. Jens Dangschat & Jorg Blasius, 1987. "Social and Spatial Disparities in Warsaw in 1978: An Application of Correspondence Analysis to a 'Socialist' City," Urban Studies, Urban Studies Journal Limited, vol. 24(3), pages 173-191, June.
    13. Shuangshuang Liu & Qipeng Liao & Mingzhu Xiao & Dengyue Zhao & Chunbo Huang, 2022. "Spatial and Temporal Variations of Habitat Quality and Its Response of Landscape Dynamic in the Three Gorges Reservoir Area, China," IJERPH, MDPI, vol. 19(6), pages 1-20, March.
    14. Vines, S.K., 2015. "Predictive nonlinear biplots: Maps and trajectories," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 47-59.
    15. K. Fernández-Aguirre & M. Garín-Martín & J. Modroño-Herrán, 2014. "Visual displays: analytical study and applications to graphs and real data," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(4), pages 2209-2224, July.
    16. J. Fernando Vera & Rodrigo Macías, 2021. "On the Behaviour of K-Means Clustering of a Dissimilarity Matrix by Means of Full Multidimensional Scaling," Psychometrika, Springer;The Psychometric Society, vol. 86(2), pages 489-513, June.
    17. Tsagris, Michail & Preston, Simon & T.A. Wood, Andrew, 2016. "Improved classi cation for compositional data using the $\alpha$-transformation," MPRA Paper 67657, University Library of Munich, Germany.
    18. Ida Camminatiello & Antonello D’Ambra & Luigi D’Ambra, 2022. "The association in two-way ordinal contingency tables through global odds ratios," METRON, Springer;Sapienza Università di Roma, vol. 80(1), pages 9-22, April.
    19. Maurizio Vichi, 2017. "Disjoint factor analysis with cross-loadings," 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(3), pages 563-591, September.
    20. J. Vera & Rodrigo Macías & Willem Heiser, 2013. "Cluster Differences Unfolding for Two-Way Two-Mode Preference Rating Data," Journal of Classification, Springer;The Classification Society, vol. 30(3), pages 370-396, October.

    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:53:y:2009:i:8:p:3103-3106. 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.