IDEAS home Printed from https://ideas.repec.org/p/zbw/sfb475/200475.html
   My bibliography  Save this paper

Latent Factor Prediction Pursuit for Rank Deficient Regressors

Author

Listed:
  • Luebke, Karsten
  • Czogiel, Irina
  • Weihs, Claus

Abstract

In simulation studies Latent Factor Prediction Pursuit outperformed classical reduced rank regression methods. The algorithm described so far for Latent Factor Prediction Pursuit had two shortcomings: It was only implemented for situations where the explanatory variables were of full colum rank. Also instead of the projection matrix only the regression matrix was calculated. These problems are addressed by a new algorithm which finds the prediction optimal projection.

Suggested Citation

  • Luebke, Karsten & Czogiel, Irina & Weihs, Claus, 2004. "Latent Factor Prediction Pursuit for Rank Deficient Regressors," Technical Reports 2004,75, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    • Handle: RePEc:zbw:sfb475:200475
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/22588/1/tr75-04.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Weihs, Claus & Hothorn, Torsten, 2002. "Determination of optimal prediction oriented multivariate latent factor models using loss functions," Technical Reports 2002,15, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    2. Eddelbuttel, Dirk, 1996. "Object-Oriented Econometrics: Matrix Programming in C++ Using GCC and NEWMAT," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(2), pages 199-209, March-Apr.
    3. Almoy, Trygve, 1996. "A simulation study on comparison of prediction methods when only a few components are relevant," Computational Statistics & Data Analysis, Elsevier, vol. 21(1), pages 87-107, January.
    4. Leo Breiman & Jerome H. Friedman, 1997. "Predicting Multivariate Responses in Multiple Linear Regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(1), pages 3-54.
    5. Weihs, Claus & Luebke, Karsten, 2004. "A Note on the Dimension of the Projection Space in a Latent Factor Regression Model with Application to Business Cycle Classification," Technical Reports 2004,29, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    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. Paul Hewson & Keming Yu, 2008. "Quantile regression for binary performance indicators," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 24(5), pages 401-418, September.
    2. Jewson Stephen & Penzer Jeremy, 2006. "Estimating Trends in Weather Series: Consequences for Pricing Derivatives," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(3), pages 1-17, September.
    3. Luebke, Karsten & Weihs, Claus, 2004. "Generation of prediction optimal projection on latent factors by a stochastic search algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 297-310, September.
    4. Adel Javanmard & Jingwei Ji & Renyuan Xu, 2024. "Multi-Task Dynamic Pricing in Credit Market with Contextual Information," Papers 2410.14839, arXiv.org, revised Oct 2024.
    5. Seokhyun Chung & Raed Al Kontar & Zhenke Wu, 2022. "Weakly Supervised Multi-output Regression via Correlated Gaussian Processes," INFORMS Joural on Data Science, INFORMS, vol. 1(2), pages 115-137, October.
    6. Qiang Sun & Hongtu Zhu & Yufeng Liu & Joseph G. Ibrahim, 2015. "SPReM: Sparse Projection Regression Model For High-Dimensional Linear Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 289-302, March.
    7. Jeongsub Choi & Mengmeng Zhu & Jihoon Kang & Myong K. Jeong, 2024. "Convolutional neural network based multi-input multi-output model for multi-sensor multivariate virtual metrology in semiconductor manufacturing," Annals of Operations Research, Springer, vol. 339(1), pages 185-201, August.
    8. Simila, Timo & Tikka, Jarkko, 2007. "Input selection and shrinkage in multiresponse linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 406-422, September.
    9. Flandoli, F. & Giorgi, E. & Aspinall, W.P. & Neri, A., 2011. "Comparison of a new expert elicitation model with the Classical Model, equal weights and single experts, using a cross-validation technique," Reliability Engineering and System Safety, Elsevier, vol. 96(10), pages 1292-1310.
    10. Gawusu, Sidique & Ahmed, Abubakari, 2024. "Analyzing variability in urban energy poverty: A stochastic modeling and Monte Carlo simulation approach," Energy, Elsevier, vol. 304(C).
    11. ter Braak, Cajo J.F., 2006. "Bayesian sigmoid shrinkage with improper variance priors and an application to wavelet denoising," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1232-1242, November.
    12. Lee, Wonyul & Liu, Yufeng, 2012. "Simultaneous multiple response regression and inverse covariance matrix estimation via penalized Gaussian maximum likelihood," Journal of Multivariate Analysis, Elsevier, vol. 111(C), pages 241-255.
    13. Dirk Eddelbuettel, 2000. "Econometrics with Octave," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 531-542.
    14. Bingzhen Chen & Wenjuan Zhai & Lingchen Kong, 2022. "Variable selection and collinearity processing for multivariate data via row-elastic-net regularization," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(1), pages 79-96, March.
    15. Zehua Chen & Yiwei Jiang, 2020. "A two-stage sequential conditional selection approach to sparse high-dimensional multivariate regression models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(1), pages 65-90, February.
    16. Stinstra, E., 2006. "The meta-model approach for simulation-based design optimization," Other publications TiSEM 713f828a-4716-4a19-af00-e, Tilburg University, School of Economics and Management.
    17. Ng, Serena, 2013. "Variable Selection in Predictive Regressions," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 752-789, Elsevier.
    18. Jae Sang Moon & Lance Manuel & Matthew J. Churchfield & Sang Lee & Paul S. Veers, 2017. "Toward Development of a Stochastic Wake Model: Validation Using LES and Turbine Loads," Energies, MDPI, vol. 11(1), pages 1-34, December.
    19. 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.
    20. Weihs, Claus & Luebke, Karsten, 2005. "Prediction Optimal Classification of Business Phases," Technical Reports 2005,41, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

    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:zbw:sfb475:200475. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/isdorde.html .

    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.