Kernel-based Censored Varying Coefficient Regression Using Miller’s Method
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초록
The censored regression model generally assumes the logarithm of survival time is modelled linearly in the covariates. In this study a censored varying coefficient regression model is proposed to consider situations in which the regression coefficients are not constant and change as the relevant smoothing variables change. Using the formulation of weighted least squares support vector machine with jumps of the Kaplan-Meier estimator of the empirical distribution of function of residuals similar to Miller’s estimation for censored regression, we can easily obtain the estimators of the proposed model through simple linear equations, and can also easily derive a generalized cross validation function. The proposed method is evaluated through simulated and real data sets.
Abstract
The censored regression model generally assumes the logarithm of survival time is modelled linearly in the covariates. In this study a censored varying coefficient regression model is proposed to consider situations in which the regression coefficients are not constant and change as the relevant smoothing variables change. Using the formulation of weighted least squares support vector machine with jumps of the Kaplan-Meier estimator of the empirical distribution of function of residuals similar to Miller’s estimation for censored regression, we can easily obtain the estimators of the proposed model through simple linear equations, and can also easily derive a generalized cross validation function. The proposed method is evaluated through simulated and real data sets.
참고문헌 (20)
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