• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.167-175
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• 2003

In this paper we develop a robust procedure to estimate regression coefficients for a linear model with censored and truncated data based on simplicial regression depth. Simplicial depth of a point is defined as the proportion of data simplices containing it. This simplicial depth can be extended to regression problem with censored and truncated data. Any line can be given a depth and the deepest regression line is the line with the maximum simplicial regression depth. We show how the proposed regression performs through analyzing AIDS incubation data.

• Journal of the Korean Data and Information Science Society
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• v.13 no.1
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• pp.157-164
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• 2002

In this paper, we consider the strong consistency of the regression quantiles estimators for the nonlinear regression models when dependent variables are subject to censoring, and provide the sufficient conditions which ensure the strong consistency of proposed estimators of the censored regression models. one example is given to illustrate the application of the main result.

• 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 Data and Information Science Society
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• v.19 no.4
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• pp.1247-1253
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• 2008

In this paper, we propose a interval censored regression spline model with a variance function (non-constant variance that depends on a predictor). Simulation studies show our estimates from MCECM algorithm are consistent, but biased when the sample size is small because of boundary effects. Also, we examined how the distribution of $x_i$ affects the converging speed of these consistent estimates.

• Journal of the Korean Statistical Society
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• v.33 no.1
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• pp.25-34
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• 2004

In this paper we propose a prediction method on the regression model with randomly censored observations of the training data set. The least squares support vector machine regression is applied for the regression function prediction by incorporating the weights assessed upon each observation in the optimization problem. Numerical examples are given to show the performance of the proposed prediction method.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.2
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• pp.39-43
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• 1999

We consider generalized exponential regression model with randomly censored data and propose a modified Fisher scoring method which estimates the model parameters. For this, the likelihood equations are derived and then the estimating algorithm is developed. We illustrate the proposed method using a real data.

• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1653-1660
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• 2016

Smoothly clipped absolute deviation (SCAD) penalty is known to satisfy the desirable properties for penalty functions like as unbiasedness, sparsity and continuity. In this paper, we deal with the regression function estimation and variable selection based on SCAD penalized censored regression model. We use the local linear approximation and the iteratively reweighted least squares algorithm to solve SCAD penalized log likelihood function. The proposed method provides an efficient method for variable selection and regression function estimation. The generalized cross validation function is presented for the model selection. Applications of the proposed method are illustrated through the simulated and a real example.

• 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.

• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.243-248
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• 2004

The timings of two successive events of interest may not be measurable, instead it may be right censored or interval censored; this data structure is called doubly censored data. In the study of HIV, two such events are the infection with HIV and the onset of AIDS. These data have been analyzed by authors under the assumption that infection time and induction time are independent. This paper investigates the regression problem when two events arc modeled to allow the presence of a possible relation between two events as well as a subject-specific effect. We derive the estimation procedure based on Goetghebeur and Ryan's (2000) piecewise exponential model and Gauss-Hermite integration is applied in the EM algorithm. Simulation studies are performed to investigate the small-sample properties and the method is applied to a set of doubly censored data from an AIDS cohort study.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.765-776
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• 2006

In this paper we propose an estimation method on the regression model with randomly censored observations of the training data set. The weighted least squares support vector machine regression is applied for the regression function estimation by incorporating the weights assessed upon each observation in the optimization problem. Numerical examples are given to show the performance of the proposed estimation method.