Journal of the Korean Data and Information Science Society

한국데이터정보과학회 (The Korean Data and Information Science Society)
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Variable selection in censored kernel regression

  • 투고 : 2012.12.11
  • 심사 : 2013.01.02
  • 발행 : 2013.01.31
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초록

For censored regression, it is often the case that some input variables are not important, while some input variables are more important than others. We propose a novel algorithm for selecting such important input variables for censored kernel regression, which is based on the penalized regression with the weighted quadratic loss function for the censored data, where the weight is computed from the empirical survival function of the censoring variable. We employ the weighted version of ANOVA decomposition kernels to choose optimal subset of important input variables. Experimental results are then presented which indicate the performance of the proposed variable selection method.

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피인용 문헌

  1. Robust minimum distance estimation of a linear regression model with correlated errors in the presence of outliers vol.50, pp.23, 2013, https://doi.org/10.1080/03610926.2020.1734831