IDEAS home Printed from https://ideas.repec.org/a/spr/psycho/v57y1992i2p237-259.html
   My bibliography  Save this article

Influence in canonical correlation analysis

Author

Listed:
  • Mario Romanazzi

Abstract

No abstract is available for this item.

Suggested Citation

  • Mario Romanazzi, 1992. "Influence in canonical correlation analysis," Psychometrika, Springer;The Psychometric Society, vol. 57(2), pages 237-259, June.
    • Handle: RePEc:spr:psycho:v:57:y:1992:i:2:p:237-259
    DOI: 10.1007/BF02294507
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/BF02294507
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/BF02294507?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. Yutaka Tanaka & Yoshimasa Odaka, 1989. "Influential observations in principal factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 54(3), pages 475-485, September.
    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. Iaci, Ross & Sriram, T.N., 2013. "Robust multivariate association and dimension reduction using density divergences," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 281-295.
    2. Adrover, Jorge G. & Donato, Stella M., 2015. "A robust predictive approach for canonical correlation analysis," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 356-376.
    3. Alam, Md. Ashad & Calhoun, Vince D. & Wang, Yu-Ping, 2018. "Identifying outliers using multiple kernel canonical correlation analysis with application to imaging genetics," Computational Statistics & Data Analysis, Elsevier, vol. 125(C), pages 70-85.
    4. Cristophe Croux & Catherine Dehon, 2003. "Estimators of the multiple correlation coefficient: Local robustness and confidence intervals," Statistical Papers, Springer, vol. 44(3), pages 315-334, July.
    5. Tanaka, Yutaka & Zhang, Fanghong & Mori, Yuichi, 2003. "Local influence in principal component analysis: relationship between the local influence and influence function approaches revisited," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 143-160, October.

    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. Sik-Yum Lee & S. Wang, 1996. "Sensitivity analysis of structural equation models," Psychometrika, Springer;The Psychometric Society, vol. 61(1), pages 93-108, March.
    2. Pison, Greet & Rousseeuw, Peter J. & Filzmoser, Peter & Croux, Christophe, 2003. "Robust factor analysis," Journal of Multivariate Analysis, Elsevier, vol. 84(1), pages 145-172, January.
    3. Sik-Yum Lee & Liang Xu, 2003. "Case-Deletion Diagnostics for Factor Analysis Models With Continuous and Ordinal Categorical Data," Sociological Methods & Research, , vol. 31(3), pages 389-419, February.
    4. Sik-Yum Lee & Liang Xu, 2003. "On local influence analysis of full information item factor models," Psychometrika, Springer;The Psychometric Society, vol. 68(3), pages 339-360, September.
    5. Sik-Yum Lee & Nian-Sheng Tang, 2004. "Local influence analysis of nonlinear structural equation models," Psychometrika, Springer;The Psychometric Society, vol. 69(4), pages 573-592, December.
    6. Ke-Hai Yuan & Linda Marshall & Peter Bentler, 2002. "A unified approach to exploratory factor analysis with missing data, nonnormal data, and in the presence of outliers," Psychometrika, Springer;The Psychometric Society, vol. 67(1), pages 95-121, March.
    7. Tanaka, Yutaka & Zhang, Fanghong & Mori, Yuichi, 2003. "Local influence in principal component analysis: relationship between the local influence and influence function approaches revisited," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 143-160, October.
    8. W. Fung & C. Kwan, 1995. "Sensitivity analysis in factor analysis: Difference between using covariance and correlation matrices," Psychometrika, Springer;The Psychometric Society, vol. 60(4), pages 607-614, December.
    9. Lu, Bin & Song, Xin-Yuan, 2006. "Local influence analysis of multivariate probit latent variable models," Journal of Multivariate Analysis, Elsevier, vol. 97(8), pages 1783-1798, September.

    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:psycho:v:57:y:1992:i:2:p:237-259. 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.