IDEAS home Printed from https://ideas.repec.org/a/eee/jmvana/v84y2003i2p369-386.html
   My bibliography  Save this article

The effect of across-location heteroscedasticity on the classification of mixed categorical and continuous data

Author

Listed:
  • Leung, Chi-Ying

Abstract

Classification of mixed categorical and continuous data is often performed using the location linear discriminant function which assumes across-location homoscedasticity. In this paper, we investigate the hazard arising from a routine application of the classifier under across-location heteroscedasticity. A limiting and a first-order asymptotic performance index are proposed and studied in a general setting. The first index studies the limiting behavior. The second index corrects the bias due to the finite sample size. Both indexes are illustrated under the assumption of unequal spherical covariance matrices across all the locations. This is likely to be the case in most classification problems dealing with mixed categorical and continuous data. Results of a numerical study are reported.

Suggested Citation

  • Leung, Chi-Ying, 2003. "The effect of across-location heteroscedasticity on the classification of mixed categorical and continuous data," Journal of Multivariate Analysis, Elsevier, vol. 84(2), pages 369-386, February.
    • Handle: RePEc:eee:jmvana:v:84:y:2003:i:2:p:369-386
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0047-259X(02)00057-X
    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. Leung, Chi-Ying, 1996. "The location linear discriminant for classifying observations with unequal variances," Statistics & Probability Letters, Elsevier, vol. 31(1), pages 23-29, December.
    2. Leung, Chi-Ying, 1996. "Error rates for classifying observations based on binary and continuous variables with covariates," Computational Statistics & Data Analysis, Elsevier, vol. 21(6), pages 627-645, June.
    3. W. Krzanowski, 1993. "The location model for mixtures of categorical and continuous variables," Journal of Classification, Springer;The Classification Society, vol. 10(1), pages 25-49, January.
    4. Chi-Ying Leung, 1998. "The Covariance Adjusted Location Linear Discriminant Function for Classifying Data with Unequal Dispersion Matrices in Different Locations," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 50(3), pages 417-431, September.
    5. N. Balakrishnan & M. Tiku, 1988. "Robust classification procedures based on dichotomous and continuous variables," Journal of Classification, Springer;The Classification Society, vol. 5(1), pages 53-80, March.
    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. Leung, Chi-Ying, 2005. "Regularized classification for mixed continuous and categorical variables under across-location heteroscedasticity," Journal of Multivariate Analysis, Elsevier, vol. 93(2), pages 358-374, April.

    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. Chi-Ying Leung, 2001. "Error rates in classification consisting of discrete and continuous variables in the presence of covariates," Statistical Papers, Springer, vol. 42(2), pages 265-273, April.
    2. Leung, Chi-Ying, 2005. "Regularized classification for mixed continuous and categorical variables under across-location heteroscedasticity," Journal of Multivariate Analysis, Elsevier, vol. 93(2), pages 358-374, April.
    3. A. R. de Leon & A. Soo & T. Williamson, 2011. "Classification with discrete and continuous variables via general mixed-data models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(5), pages 1021-1032, February.
    4. Thomas Bittmann & Jens‐Peter Loy & Sven Anders, 2020. "Product differentiation and cost pass‐through: industry‐wide versus firm‐specific cost shocks," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 64(4), pages 1184-1209, October.
    5. Colin O. Wu & Gang Zheng & Minjung Kwak, 2013. "A Joint Regression Analysis for Genetic Association Studies with Outcome Stratified Samples," Biometrics, The International Biometric Society, vol. 69(2), pages 417-426, June.
    6. Leung, Chi-Ying, 1999. "Covariance Adjustments in Discrimination of Mixed Discrete and Continuous Variables," Journal of Multivariate Analysis, Elsevier, vol. 71(1), pages 111-124, October.
    7. Sudha Bishnoi & Nadhir Al-Ansari & Mujahid Khan & Salim Heddam & Anurag Malik, 2022. "Classification of Cotton Genotypes with Mixed Continuous and Categorical Variables: Application of Machine Learning Models," Sustainability, MDPI, vol. 14(20), pages 1-17, October.
    8. Layal Christine Lettry, 2023. "Clustering the Swiss Pension Register," FSES Working Papers 529, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    9. Nor Mahat & W.J. Krzanowski & A. Hernandez, 2009. "Strategies for Non-Parametric Smoothing of the Location Model in Mixed-Variable Discriminant Analysis," Modern Applied Science, Canadian Center of Science and Education, vol. 3(1), pages 151-151, January.
    10. Merbouha, A. & Mkhadri, A., 2004. "Regularization of the location model in discrimination with mixed discrete and continuous variables," Computational Statistics & Data Analysis, Elsevier, vol. 45(3), pages 563-576, April.
    11. Pan Shenyi & Joe Harry, 2024. "Assessing copula models for mixed continuous-ordinal variables," Dependence Modeling, De Gruyter, vol. 12(1), pages 1-18.
    12. W. Krzanowski, 1993. "The location model for mixtures of categorical and continuous variables," Journal of Classification, Springer;The Classification Society, vol. 10(1), pages 25-49, January.

    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:jmvana:v:84:y:2003:i:2:p:369-386. 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/wps/find/journaldescription.cws_home/622892/description#description .

    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.