Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
권호 표지

Mixed-effects LS-SVR for longitudinal dat

  • Received : 2010.02.12
  • Accepted : 2010.03.23
  • Published : 2010.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper we propose a mixed-effects least squares support vector regression (LS-SVR) for longitudinal data. We add a random-effect term in the optimization function of LS-SVR to take random effects into LS-SVR for analyzing longitudinal data. We also present the model selection method that employs generalized cross validation function for choosing the hyper-parameters which affect the performance of the mixed-effects LS-SVR. A simulated example is provided to indicate the usefulness of mixed-effect method for analyzing longitudinal data.

Keywords

References

  1. Allen, J. and Murray, A. (1993). Development of a neural network screening aid for diagnosing lower limb peripheral vascular disease from photoelectric plethysmography pulse waveforms. Physiological Measurement, 14, 13-22. https://doi.org/10.1088/0967-3334/14/1/003
  2. Christianini, N. and Shawe-Taylor, J. (2000). An introduction to support vector machines, Cambridge University Press, Cambridge.
  3. Guler, N. F. and Kocer, S. (2005). Use of support vector machines and neural network in diagnosis of neuromuscular disorders. Journal of Medical System, 29, 271-84. https://doi.org/10.1007/s10916-005-5187-4
  4. Hedeker, D. and Gibbons, R. D. (2006). Longitudinal data Analysis, John Wiley & Sons, New York.
  5. Hwang, C. (2008). Mixed effects kernel binomial regression. Journal of Korean Data & Information Science Society, 19, 1327-1334.
  6. Liu, H. X., Zhang, R. S., Luan, F., Yao, X. J., Liu, M. C., Hu, Z. D. and Fan, B. T. (2003). Diagnosing breast cancer based on support vector machines. Journal of Chemical Information and Computer Sciences, 43, 900-907. https://doi.org/10.1021/ci0256438
  7. Mercer, J. (1909). Functions of positive and negative type and their connection with theory of integral equations. Philosophical Transactions of Royal Society, A, 415-446.
  8. Shim, J. and Lee, J. T. (2009). Kernel method for autoregressive data. Journal of Korean Data & Information Science Society, 20, 949-964 .
  9. Shim, J., Park, H. J. and Seok, K. H. (2008). Kernel Poisson regression for longitudinal data. Journal of Korean Data & Information Science Society, 19, 1353-1360.
  10. Shim, J. and Seok, K. H. (2009). Variance function estimation with LS-SVM for replicated data. Journal of Korean Data & Information Science Society, 20, 925 -931
  11. Suykens, J. A. K. and Vanderwalle, J. (1999). Least square support vector machine classifier. Neural Processing Letters, 9, 293-300. https://doi.org/10.1023/A:1018628609742
  12. Suykens, J. A. K., Vanderwalle, J. and De Moor, B. (2001) Optimal control by least squares support vector machines. Neural Networks, 14, 23-35. https://doi.org/10.1016/S0893-6080(00)00077-0
  13. Vapnik, V. N. (1998). Statistical learning theory, John Wiley, New York.
  14. Wahba, G. (1990). Spline models for observational data. SIAM, Philadelphia. CMMS-NSF Regional Conference Series in Applied Mathematics, 59.