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Longitudinal Data Analysis Using Liu Regression

Author

Listed:
  • Ramalingam Shanmugam

    (Department of Statistics, Shahrood University of Technology, Shahrood, Iran)

  • Arashi M

    (Department of Statistics, Shahrood University of Technology, Shahrood, Iran)

  • Salarzadeh Jenatabadi H

    (Department of Science and Technology Studies, University of Malaya, Malaysia)

Abstract

For understanding and characterizing discase progression over time, Eliot et al. [1] proposed a mixed ridge regression to account correlated outcomes and potentially high degree of correlated predictors for Biomarker data. However, the ridge estimator is non-linear in nature w.r.t. the ridge parameter and hence it is hard to estimate In this paper, we propose a linear unified approach to combat this difficultly. Numerical studies illustrate the usefulness of our approach compared to the mixed model.

Suggested Citation

  • Ramalingam Shanmugam & Arashi M & Salarzadeh Jenatabadi H, 2018. "Longitudinal Data Analysis Using Liu Regression," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 7(5), pages 102-105, July.
    • Handle: RePEc:adp:jbboaj:v:7:y:2018:i:5:p:102-105
    DOI: 10.19080/BBOAJ.2018.07.555725
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    References listed on IDEAS

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    1. Eliot Melissa & Ferguson Jane & Reilly Muredach P. & Foulkes Andrea S., 2011. "Ridge Regression for Longitudinal Biomarker Data," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-11, September.
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    Cited by:

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