• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.957-968
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• 2021

Inverse censoring probability weighting (ICPW) is a popular technique in survival data analysis. In applications of the ICPW technique such as the censored regression, it is crucial to accurately estimate the censoring probability. A simulation study is undertaken in this article to see how censoring probability estimate influences model performance in censored regression using the ICPW scheme. We compare three censoring probability estimators, including Kaplan-Meier (KM) estimator, Cox proportional hazard model estimator, and local KM estimator. For the local KM estimator, we propose to reduce the predictor dimension to avoid the curse of dimensionality and consider two popular dimension reduction tools: principal component analysis and sliced inverse regression. Finally, we found that the Cox proportional hazard model estimator shows the best performance as a censoring probability estimator in both mean and median censored regressions.

• Journal of Korean Society for Quality Management
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• v.24 no.4
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• pp.44-58
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• 1996

This paper presents optimal accelerated life test plans with different censoring times for exponential, Weibull, and lognormal lifetime distributions, respectively. For an optimal plan, low stress level, proportion of test units allocated and censoring time at each stress are determined such that the asymptotic variance of the maximum likelihood estimator of a certain quantile at use condition is minimized. The proposed plans are compared with the corresponding optimal plans with a common censoring time over range of parameter values. Computational results indicate that those plans are statistically optimal ones in terms of accuracy of estimator when total censoring times of two plans are equal.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.127-135
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• 2001

In this paper, we consider a simple method for testing the assumption of independent censoring on the basis of a Cox proportional hazards regression model with a time-dependent covariate. This method involves a two-stage sampling in which a random subset of censored observations is selected and followed-up until their true survival times are observed. Lee and Wolfe(1998) proposed an adjusted estimate of the survivor function for the dependent censoring under a proportional hazards alternative. This paper extends their result to obtain a bootstrap confidence interval for the adjusted survivor function under the dependent censoring. The proposed procedure is illustrated with an example of a clinical trial for lung cancer analysed in Lee and Wolfe(1998).

• Communications for Statistical Applications and Methods
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• v.15 no.5
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• pp.765-774
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• 2008

In this paper, we derive the approximate maximum likelihood estimators(AMLEs) and maximum likelihood estimator of the scale parameter in a triangular distribution based on progressive Type-II censored samples. We compare the proposed estimators in the sense of the mean squared error through Monte Carlo simulation for various progressive censoring schemes.

• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.807-818
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• 2005

In this paper we propose an estimation method for regression quantiles with left-truncated and right-censored data. The estimation procedure is based on the weight determined by the Kaplan-Meier estimate of the distribution of the response. We show how the proposed regression quantile estimators perform through analyses of Stanford heart transplant data and AIDS incubation data. We also investigate the effect of censoring on regression quantiles through simulation study.

• Journal of the Korean Statistical Society
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• v.25 no.1
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• pp.133-144
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• 1996

We, in this paper, propose a nonparametric estimator of bivariate mean residual life function based on Lin and Ying's (1993) bivariate survival function estimator of paired failure times under univariate censoring and prove the uniform consistency and the weak convergence result of this estimator. Through Monte Carlo simulation, the performances of the proposed estimator are tabulated and are illustrated with the skin grafts data.

• Journal of the Korean Data and Information Science Society
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• v.6 no.2
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• pp.65-76
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• 1995

The importance of left truncated and right censoring cases has considered for better information in medical follow-up and engineering life testing studies. We propose some estimation procedure for the mean residual life function with consistency and asymptotic normality on the left truncated and right censoring model. And then, the comparision with Kaplan-Meier estimator ignoring the left truncated effect and the small sample properities are investigated by asymptotic biases and M.S.E.'s thresh Monte Carlo study.

• Journal of the Korean Data and Information Science Society
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• v.8 no.1
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• pp.99-109
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• 1997

The estimation procedure of mean residual life function has been placed an important role in the study of survival analysis. In this paper, the product limit estimator on left truncated and right censoring model is proposed with asymptotic properties. Also, the small sample properties are investigated through the Monte Carlo study and the proposed product limit type estimator is compared with ordinary Kaplan-Meier type estimator.

• ETRI Journal
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• v.29 no.1
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• pp.110-112
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• 2007

As an alternative to the classic linear detector which only assumes noise variance, a new robust detector with noise variance estimation censoring input signals over AWGN is proposed. The results demonstrate that analytic detection probability matches the simulation results for the linear detector and that the new robust detector shows better performance than the linear detector when the number of samples increases.

• Journal of the Korean Data and Information Science Society
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• v.10 no.2
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• pp.405-413
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• 1999

By assuming a type-II censoring, we propose the approximate maximum likelihood estimators (AMLEs) of the location and the scale parameters of the two-parameter Rayleigh distribution and calculate the asymptotic variances and covariance of the AMLEs.