Communications for Statistical Applications and Methods

  • 제13권3호
  • /
  • Pages.765-776
  • /
  • 2006
  • /
  • 2287-7843(pISSN)
  • /
  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
권호 표지
DOI QR코드

DOI QR Code

Weighted LS-SVM Regression for Right Censored Data

  • Kim, Dae-Hak (Department of Statistics Information, Catholic University of Daegu) ;
  • Jeong, Hyeong-Chul (Department of Applied Statistics, The University of Suwon)
  • 발행 : 2006.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

In this paper we propose an estimation method on the regression model with randomly censored observations of the training data set. The weighted least squares support vector machine regression is applied for the regression function estimation by incorporating the weights assessed upon each observation in the optimization problem. Numerical examples are given to show the performance of the proposed estimation method.

키워드

참고문헌

  1. Fygenson, M and Zhou, M. (1992). Modifying the Koul, Susarla and Van Ryzin estimator for linear regression models with right censoring. Statistics & Probability Letters, Vol. 13, 295-299 https://doi.org/10.1016/0167-7152(92)90037-6
  2. Kaplan, E.L. and Meier, P. (1958). Nonparametric estimation from incomplete observations. Joumal of American Statistical Association, Vol. 66, 484-491
  3. Koul, H., Susarla, V. and Van Ryzin J, (1981). Regression Analysis with Randomly Right Censored Data. The Annals of Statistics, Vol. 9, 1276-1288 https://doi.org/10.1214/aos/1176345644
  4. Leurgans, S. (1987). Linear models, random censoring and synthetic data. Biometrika, Vol. 74, 301-9 https://doi.org/10.1093/biomet/74.2.301
  5. 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
  6. Miller, R. and Halpern, J. (1982). Regression with Censored Data. Biometrika, Vol. 69, 521-531 https://doi.org/10.1093/biomet/69.3.521
  7. Suykens, J.A.K. and Vanderwalle, J. (1999). Least Square Support Vector Machine Classifier. Neural Processing Letters, Vol. 9, 293-300 https://doi.org/10.1023/A:1018628609742
  8. Vapnik, V.N. (1995). The Nature of Statistical Learning Theory. Springer, New York
  9. Vapnik, V.N. (1998). Statistical Learning Theory, Springer, New York
  10. Zhou, M (1992). M-estimation in Censored Linear Models. Biometrika, Vol. 79, 837-841 https://doi.org/10.1093/biomet/79.4.837