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Doubly Regularized Kernel Regression with Heteroscedastic Censored Data

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Advances in Natural Computation (ICNC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3610))

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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.

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© 2005 Springer-Verlag Berlin Heidelberg

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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

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  • 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)

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