IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v50y2006i3p642-658.html

An even faster algorithm for ridge regression of reduced rank data

Author

Listed:
  • Turlach, Berwin A.

Abstract

No abstract is available for this item.

Suggested Citation

  • Turlach, Berwin A., 2006. "An even faster algorithm for ridge regression of reduced rank data," Computational Statistics & Data Analysis, Elsevier, vol. 50(3), pages 642-658, February.
  • Handle: RePEc:eee:csdana:v:50:y:2006:i:3:p:642-658
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(04)00291-9
    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. Hawkins, Douglas M. & Yin, Xiangrong, 2002. "A faster algorithm for ridge regression of reduced rank data," Computational Statistics & Data Analysis, Elsevier, vol. 40(2), pages 253-262, August.
    2. S. N. Wood, 2000. "Modelling and smoothing parameter estimation with multiple quadratic penalties," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(2), pages 413-428.
    Full references (including those not matched with items on IDEAS)

    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. Karimu, Amin & Mensah, Justice Tei & Adu, George, 2016. "Who Adopts LPG as the Main Cooking Fuel and Why? Empirical Evidence on Ghana Based on National Survey," World Development, Elsevier, vol. 85(C), pages 43-57.
    2. Beran, Rudolf, 2014. "Hypercube estimators: Penalized least squares, submodel selection, and numerical stability," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 654-666.
    3. Nadja Klein & Michel Denuit & Stefan Lang & Thomas Kneib, 2013. "Nonlife Ratemaking and Risk Management with Bayesian Additive Models for Location, Scale and Shape," Working Papers 2013-24, Faculty of Economics and Statistics, Universität Innsbruck.
    4. Simon N. Wood, 2020. "Inference and computation with generalized additive models and their extensions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 307-339, June.
    5. Rüdiger Krause & Gerhard Tutz, 2006. "Genetic algorithms for the selection of smoothing parameters in additive models," Computational Statistics, Springer, vol. 21(1), pages 9-31, March.
    6. Longhi, Christian & Musolesi, Antonio & Baumont, Catherine, 2014. "Modeling structural change in the European metropolitan areas during the process of economic integration," Economic Modelling, Elsevier, vol. 37(C), pages 395-407.
    7. Simon N. Wood & Natalya Pya & Benjamin Säfken, 2016. "Smoothing Parameter and Model Selection for General Smooth Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1548-1563, October.
    8. Zwane, E. N. & van der Heijden, P. G. M., 2004. "Semiparametric models for capture-recapture studies with covariates," Computational Statistics & Data Analysis, Elsevier, vol. 47(4), pages 729-743, November.
    9. Strasak, Alexander M. & Umlauf, Nikolaus & Pfeiffer, Ruth M. & Lang, Stefan, 2011. "Comparing penalized splines and fractional polynomials for flexible modelling of the effects of continuous predictor variables," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1540-1551, April.
    10. Roberto Basile, 2009. "Productivity Polarization across Regions in Europe," International Regional Science Review, , vol. 32(1), pages 92-115, January.
    11. Musolesi Antonio & Mazzanti Massimiliano, 2014. "Nonlinearity, heterogeneity and unobserved effects in the carbon dioxide emissions-economic development relation for advanced countries," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(5), pages 521-541, December.
    12. Daly Don Simone & Anderson Kevin K & White Amanda M & Gonzalez Rachel M & Varnum Susan M & Zangar Richard C, 2008. "Predicting Protein Concentrations with ELISA Microarray Assays, Monotonic Splines and Monte Carlo Simulation," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-21, July.
    13. Paciorek, Christopher J., 2007. "Computational techniques for spatial logistic regression with large data sets," Computational Statistics & Data Analysis, Elsevier, vol. 51(8), pages 3631-3653, May.
    14. Anne-Sophie Krah & Zoran Nikoli'c & Ralf Korn, 2019. "Machine Learning in Least-Squares Monte Carlo Proxy Modeling of Life Insurance Companies," Papers 1909.02182, arXiv.org.
    15. Peter Flaschel & Göran Kauermann & Willi Semmler, 2007. "Testing Wage And Price Phillips Curves For The United States," Metroeconomica, Wiley Blackwell, vol. 58(4), pages 550-581, November.
    16. Silvia Ferrini & Carlo Fezzi, 2012. "Generalized Additive Models for Nonmarket Valuation via Revealed or Stated Preference Methods," Land Economics, University of Wisconsin Press, vol. 88(4), pages 782-802.
    17. Xue, Yuan & Yin, Xiangrong & Jiang, Xiaolin, 2016. "Ensemble sufficient dimension folding methods for analyzing matrix-valued data," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 193-205.
    18. Florackis, Chrisostomos & Kostakis, Alexandros & Ozkan, Aydin, 2009. "Managerial ownership and performance," Journal of Business Research, Elsevier, vol. 62(12), pages 1350-1357, December.
    19. Tuomala, Juha & Hämäläinen, Kari, 2007. "Vocational Labour Market Training in Promoting Youth Employment," Discussion Papers 432, VATT Institute for Economic Research.
    20. Greiner, Alfred & Kauermann, Göran, 2008. "Debt policy in euro area countries: Evidence for Germany and Italy using penalized spline smoothing," Economic Modelling, Elsevier, vol. 25(6), pages 1144-1154, November.

    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:50:y:2006:i:3:p:642-658. 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.