Abstract
A doubly regularized likelihood estimating procedure is introduced for the heteroscedastic censored regression. The proposed procedure provides the estimates of both the conditional mean and the variance of the response variables, which are obtained by two stepwise iterative fashion. The generalized cross validation function and the generalized approximate cross validation function are used alternately to estimate tuning parameters in each step. Experimental results are then presented which indicate the performance of the proposed estimating procedure.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Buckley, J., James, I.: Linear regression with censdored dara. Biometrics 30, 89–99 (1974)
Kaplan, E.L., Meier, P.: Nonparametric estimation from incomplete observations. Journal of American Statistical Association 53, 457–481 (1959)
Kim, D., Shim, J., Oh, K.: Censored Regression by LS -SVM. Journal of of the Korean Statistical Society 33(1), 25–34 (2004)
Kimeldorf, G.S., Wahba, G.: Some results on Tchebycheffian spline functions. Journal of Mathematical Analysis ans its Applications 33, 82–95 (1971)
Koul, H., Susarla, V., Van Ryzin, J.: Regression analysis with randomly right censored data. The Annal of Statistics 9, 1276–1288 (1981)
Mercer, J.: Functions of positive and negative type and their connection with the theory of integral equations. Philosophical Transactions of the Royal Society A, 415–446 (1909)
Miller, R.G., Halpern, J.: Regression with censored data. Biometrika 69, 521–531 (1982)
Saunders, C., Gammerman, A., Vovk, V.: Ridege regression learning algorithm in dual variables. In: Proceeding of 15th International Conference on Machine Learning, Madison, WI, July 24-27, pp. 515–521 (1998)
Suykens, J.A.K., Vanderwalle, J.: Least Square Support Vector Machine Classifier. Neural Processing Letters 9, 293–300 (1999)
Wahba, G.: Spline models for observational data. In: CBMS-NSF Regional Conference Series in Applied Mathematics, Society for Industrial and Applied Mathematics, Philadelphia, PA, vol. 59, p. 169 (1990)
Xiang, D., Wahba, G.: A generalized approximate cross validation for smoothing splines with non-Gaussian data. Statistica Sinica 6, 675–692 (1996)
Zhou, M.: M-estimation in censored linear models. Biometrika 79(4), 837–841 (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shim, J., Hwang, C. (2005). Doubly Regularized Kernel Regression with Heteroscedastic Censored Data. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_67
Download citation
DOI: https://doi.org/10.1007/11539087_67
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28323-2
Online ISBN: 978-3-540-31853-8
eBook Packages: Computer ScienceComputer Science (R0)