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Journal of the Korean Data & Information Science Society = 한국데이터정보과학회지 v.21 no.2, 2010년, pp.363 - 369   피인용횟수: 6
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

Mixed-effects LS-SVR for longitudinal dat

Cho, Dae-Hyeon    (Department of Data Science, Inje University  );
  • 초록

    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.


  • 주제어

    Generalized cross validation function .   hyper-parameter .   kernel function .   least squares support vector machines .   mixed-effects regression model.  

  • 참고문헌 (14)

    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. 
    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. 
    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. 
    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. and Seok, K. H. (2009). Variance function estimation with LS-SVM for replicated data. Journal of Korean Data & Information Science Society, 20, 925 -931     
    10. Suykens, J. A. K. and Vanderwalle, J. (1999). Least square support vector machine classifier. Neural Processing Letters, 9, 293-300. 
    11. Suykens, J. A. K., Vanderwalle, J. and De Moor, B. (2001) Optimal control by least squares support vector machines. Neural Networks, 14, 23-35. 
    12. Vapnik, V. N. (1998). Statistical learning theory, John Wiley, New York. 
    13. Wahba, G. (1990). Spline models for observational data. SIAM, Philadelphia. CMMS-NSF Regional Conference Series in Applied Mathematics, 59. 
    14. 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.     
  • 이 논문을 인용한 문헌 (6)

    1. Lee, Jea-Young ; Lee, Jong-Hyeong 2010. "Support vector machine and multifactor dimensionality reduction for detecting major gene interactions of continuous data" Journal of the Korean Data & Information Science Society = 한국데이터정보과학회지, 21(6): 1271~1280     
    2. Lee, Ji-Hong ; Yeo, Jung-Sou 2011. "Estimation of genetic parameters using real-time ultrasound measurements in Hanwoo" Journal of the Korean Data & Information Science Society = 한국데이터정보과학회지, 22(6): 1145~1152     
    3. Lee, Jong-Hyeong ; Lee, Jea-Young 2011. "A Comparison Study on SVM MDR and D-MDR for Detecting Gene-Gene Interaction in Continuous Data" 한국통계학회 논문집 = Communications of the Korean Statistical Society, 18(4): 413~422     
    4. Lee, Yoon-Seok ; Oh, Dong-Yep ; Yeo, Jung-Sou 2011. "Study on identification of candidate DNA marker related with beef quailty in QTL region of BTA 2 in Hanwoo population" Journal of the Korean Data & Information Science Society = 한국데이터정보과학회지, 22(4): 661~669     
    5. 2012. "" Journal of the Korean Data & Information Science Society = 한국데이터정보과학회지, 23(1): 209~218     
    6. 2012. "" Journal of the Korean Data & Information Science Society = 한국데이터정보과학회지, 23(2): 375~383     

 저자의 다른 논문

  • 조대현 (18)

    1. 1996 "Tail Probability Approximations for the Ratio of the Independent Random Variables" Journal of statistical theory & methods = 統計理論方法硏究 7 (2): 189~201    
    2. 1998 "Saddlepoint approximations for the ratio of two independent sequences of random variables" Journal of the Korean Data & Information Science Society = 한국데이터정보과학회지 9 (2): 255~262    
    3. 1999 "Tail Probability Approximations for the Ratio of two Independent Sequences of Random Variables" Journal of the Korean Data & Information Science Society = 한국데이터정보과학회지 10 (2): 415~428    
    4. 2002 "On Approximate Prediction Intervals for Support Vector Machine Regression" Journal of the Korean Data & Information Science Society = 한국데이터정보과학회지 13 (2): 65~75    
    5. 2003 "A Study on Support Vectors of Least Squares Support Vector Machine" 한국통계학회 논문집 = Communications of the Korean Statistical Society 10 (3): 873~878    
    6. 2008 "라운드로빙 방식을 응용한 복식조 편성방법" 응용통계연구 = The Korean journal of applied statistics 21 (6): 1015~1026    
    7. 2009 "실력이 순서화된 경우에 대한 복식조 편성방법" 응용통계연구 = The Korean journal of applied statistics 22 (6): 1331~1343    
    8. 2009 "화장품구매 자료를 통한 고객 구매행태 분석" Journal of the Korean Data & Information Science Society = 한국데이터정보과학회지 20 (4): 615~627    
    9. 2010 "가위바위보를 이용한 승부결정과 모의실험" 응용통계연구 = The Korean journal of applied statistics 23 (6): 1217~1224    
    10. 2010 "Doubly penalized kernel method for heteroscedastic autoregressive datay" Journal of the Korean Data & Information Science Society = 한국데이터정보과학회지 21 (1): 155~162    

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