IDEAS home Printed from https://ideas.repec.org/a/eee/jmvana/v119y2013icp47-60.html
   My bibliography  Save this article

Supervised component generalized linear regression using a PLS-extension of the Fisher scoring algorithm

Author

Listed:
  • Bry, X.
  • Trottier, C.
  • Verron, T.
  • Mortier, F.

Abstract

In the current estimation of a GLM model, the correlation structure of regressors is not used as the basis on which to lean strong predictive dimensions. Looking for linear combinations of regressors that merely maximize the likelihood of the GLM has two major consequences: (1) collinearity of regressors is a factor of estimation instability, and (2) as predictive dimensions may lean on noise, both predictive and explanatory powers of the model are jeopardized. For a single dependent variable, attempts have been made to adapt PLS regression, which solves this problem in the classical Linear Model, to GLM estimation. In this paper, we first discuss the methods thus developed, and then propose a technique, Supervised Component Generalized Linear Regression (SCGLR), that combines PLS regression with GLM estimation in the multivariate context. SCGLR is tested on both simulated and real data.

Suggested Citation

  • Bry, X. & Trottier, C. & Verron, T. & Mortier, F., 2013. "Supervised component generalized linear regression using a PLS-extension of the Fisher scoring algorithm," Journal of Multivariate Analysis, Elsevier, vol. 119(C), pages 47-60.
    • Handle: RePEc:eee:jmvana:v:119:y:2013:i:c:p:47-60
    DOI: 10.1016/j.jmva.2013.03.013
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0047259X13000407
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jmva.2013.03.013?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. Bastien, Philippe & Vinzi, Vincenzo Esposito & Tenenhaus, Michel, 2005. "PLS generalised linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 48(1), pages 17-46, 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. Stéphane Mussard & Fattouma Souissi-Benrejab, 2019. "Gini-PLS Regressions," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(3), pages 477-512, September.
    2. Meng, Ming & Mander, Sarah & Zhao, Xiaoli & Niu, Dongxiao, 2016. "Have market-oriented reforms improved the electricity generation efficiency of China's thermal power industry? An empirical analysis," Energy, Elsevier, vol. 114(C), pages 734-741.
    3. Jin Hee Bae & Minwoo Kim & J.S. Lim & Zong Woo Geem, 2021. "Feature Selection for Colon Cancer Detection Using K-Means Clustering and Modified Harmony Search Algorithm," Mathematics, MDPI, vol. 9(5), pages 1-14, March.

    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. ben Jabeur, Sami & Mefteh-Wali, Salma & Carmona, Pedro, 2021. "The impact of institutional and macroeconomic conditions on aggregate business bankruptcy," Structural Change and Economic Dynamics, Elsevier, vol. 59(C), pages 108-119.
    2. Sami Ben Jabeur & Youssef Fahmi, 2018. "Forecasting financial distress for French firms: a comparative study," Empirical Economics, Springer, vol. 54(3), pages 1173-1186, May.
    3. Basma Albanna & Julia Handl & Richard Heeks, 2021. "Publication outperformance among global South researchers: An analysis of individual-level and publication-level predictors of positive deviance," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(10), pages 8375-8431, October.
    4. Sami BEN JABEUR, 2014. "Prévision de la détresse financière des entreprises françaises: Approche par la régression logistique PLS," Working Papers 2014-321, Department of Research, Ipag Business School.
    5. Meisam Moghimbeygi & Anahita Nodehi, 2022. "Multinomial Principal Component Logistic Regression on Shape Data," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 578-599, November.
    6. Stéphane Mussard & Fattouma Souissi-Benrejab, 2019. "Gini-PLS Regressions," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(3), pages 477-512, September.
    7. Jara, Magali & Cliquet, Gérard, 2012. "Retail brand equity: Conceptualization and measurement," Journal of Retailing and Consumer Services, Elsevier, vol. 19(1), pages 140-149.
    8. Sami Ben Jabeur & Youssef Fahmi, 2014. "Les modèles de prévision de la défaillance des entreprises françaises : une approche comparative," Working Papers 2014-317, Department of Research, Ipag Business School.
    9. Aguilera, Ana M. & Escabias, Manuel & Valderrama, Mariano J., 2006. "Using principal components for estimating logistic regression with high-dimensional multicollinear data," Computational Statistics & Data Analysis, Elsevier, vol. 50(8), pages 1905-1924, April.
    10. Tenenhaus, Arthur & Giron, Alain & Viennet, Emmanuel & Bera, Michel & Saporta, Gilbert & Fertil, Bernard, 2007. "Kernel logistic PLS: A tool for supervised nonlinear dimensionality reduction and binary classification," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4083-4100, May.
    11. Marie-Laure Mourre & Patricia Gurviez, 2015. "Proposition d’un modèle intégrateur de la résistance aux messages anti-tabac," Post-Print hal-01443801, HAL.
    12. Chung, Henry F.L. & Rose, Ellen & Huang, Pei-how, 2012. "Linking international adaptation strategy, immigrant effect, and performance: The case of home–host and cross-market scenario," International Business Review, Elsevier, vol. 21(1), pages 40-58.
    13. Younes Ben Zaied & Siagh Ahmed Ramzi, 2012. "Les déterminants du succès entrepreneurial ; Une étude empirique de la région de Sfax en Tunisie," Economics Working Paper Archive (University of Rennes & University of Caen) 201235, Center for Research in Economics and Management (CREM), University of Rennes, University of Caen and CNRS.
    14. Kawano, Shuichi & Fujisawa, Hironori & Takada, Toyoyuki & Shiroishi, Toshihiko, 2018. "Sparse principal component regression for generalized linear models," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 180-196.
    15. Ben Jabeur Sami, 2013. "Corporate Failure:A Non Parametric Method," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 2(3), pages 103-110, July.
    16. Asma Sghaier & Sami Ben Jabeur & Boutheina Bannour, 2018. "Using partial least square discriminant analysis to distinguish between Islamic and conventional banks in the MENA region," Review of Financial Economics, John Wiley & Sons, vol. 36(2), pages 133-148, April.
    17. Ana M. Aguilera, 2016. "Comments on: Probability enhanced effective dimension reduction for classifying sparse functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(1), pages 23-26, March.
    18. Magali Jara, 2009. "« Retail Brand Equity: A PLS Approach," Post-Print halshs-00413604, HAL.
    19. Ben Jabeur, Sami, 2017. "Bankruptcy prediction using Partial Least Squares Logistic Regression," Journal of Retailing and Consumer Services, Elsevier, vol. 36(C), pages 197-202.

    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:119:y:2013:i:c:p:47-60. 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.