• Communications for Statistical Applications and Methods
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• v.23 no.6
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• pp.555-562
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• 2016

Interval censored data often occur in an observational study where the subject is followed periodically. Instead of observing an exact failure time, two inspection times that include it are available. There are several methods to analyze interval censored failure time data (Sun, 2006). However, in the presence of competing risks, few methods have been suggested to estimate covariate effect on interval censored competing risk data. A sub-distribution hazard model is a commonly used regression model because it has one-to-one correspondence with a cumulative incidence function. Alternatively, Klein and Andersen (2005) proposed a pseudo-value approach that directly uses the cumulative incidence function. In this paper, we consider an extension of the pseudo-value approach into the interval censored data to estimate regression coefficients. The pseudo-values generated from the estimated cumulative incidence function then become response variables in a generalized estimating equation. Simulation studies show that the suggested method performs well in several situations and an HIV-AIDS cohort study is analyzed as a real data example.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.201-209
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• 2013

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.

• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.1167-1177
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• 2017

The censored regression using the pseudo-response variable proposed by Buckley and James has been one of the most well-known models. Recently, the varying coefficient regression model has received a great deal of attention as an important tool for modeling. In this paper we propose a censored varying coefficient regression model using Buckley-James method to consider situations where the regression coefficients of the model are not constant but change as the smoothing variables change. By using the formulation of least squares support vector machine (LS-SVM), the coefficient estimators of the proposed model can be easily obtained from simple linear equations. Furthermore, a generalized cross validation function can be easily derived. In this paper, we evaluated the proposed method and demonstrated the adequacy through simulate data sets and real data sets.

• Journal of Korean Society for Quality Management
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• v.24 no.1
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• pp.32-43
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• 1996

In this paper we develop nonparametric methods for regression analysis when the response variable is subject to censoring that arises naturally in quality engineering. This development is based on a general missing information principle that enables us to apply, via an iterative scheme, nonparametric regression techniques for complete data to iteratively reconstructed data from a given sample with censored observations. In particular, additive regression models are extended to right-censored data. This nonparametric regression method is applied to a simulated data set and the estimated smooth functions provide insights into the relationship between failure time and explanatory variables in the data.

• Journal of the Korean Data and Information Science Society
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• v.13 no.2
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• pp.31-38
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• 2002

In the case of multiple survival times which might be censored at each covariate vector, we study the regression quantile estimations in this paper. The estimations are based on the empirical distribution functions of the censored times and the sample quantiles of the observed survival times at each covariate vector and the weighted least square method is applied for the estimation of the regression quantile. The estimators are shown to be asymptotically normally distributed under some regularity conditions.

• Bulletin of the Korean Mathematical Society
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• v.34 no.4
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• pp.573-581
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• 1997

This paper is concerned with the strong consistency of the $L_1$-norm estimators for the nonlinear regression models when dependent variables are subject to censoring, and provides the sufficient conditions which ensure the strong consistency of $L_1$-norm estimators of the censored regression models.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.951-959
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• 2011

The proposed method is based on a penalized censored regression model with L1-penalty. We use the iteratively reweighted least squares procedure to solve L1 penalized log likelihood function of censored regression model. It provide the efficient computation of regression parameters including variable selection and leads to the generalized cross validation function for the model selection. Numerical results are then presented to indicate the performance of the proposed method.

• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.237-241
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• 2004

In standard time-to-event or survival analysis, the occurrence times of the event of interest are observed exactly or are right-censored. However in certain situations such as the AIDS data, the incubation period which is the time between HIV infection time and the diagnosis of AIDS is usually doubly censored. That is the HIV infection time Is interval censored and also the time of the diagnosis of AIDS is right censored. In this paper, we Impute the Interval censored infection time using the conditional mean imputation and estimate the coefficient factor of the regression analysis for the incubation period using Gibbs sampler. We applied parametric and semi-parametric methods for the analysis of the Incubation period and compared the results.

• Communications for Statistical Applications and Methods
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• v.5 no.3
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• pp.733-742
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• 1998

For industrial and medical lifetime data, the generalized log-gamma regression model is considered. Then the Bayesian analysis for the generalized log-gamma regression with censored data are explained and following the data augmentation (Tanner and Wang; 1987), the censored data is replaced by simulated data. To overcome the complicated Bayesian computation, Makov Chain Monte Carlo (MCMC) method is employed. Then some modified algorithms are proposed to implement MCMC. Finally, one example is presented.

• Journal of the Korean Statistical Society
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• v.27 no.1
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• pp.101-112
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• 1998

This paper is concerned with the asymptotic properties of the least absolute deviation estimators for the nonlinear regression model when dependent variables are subject to censoring time, and proposed the simple and practical sufficient conditions for the strong consistency and asymptotic normality of the least absolute deviation estimators in censored regression model. Some desirable asymptotic properties including the asymptotic relative efficiency of proposed model with respect to standard model are given.