• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.313-322
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• 2007

Several robust censored depth regression methods are compared under contamination. Park and Hwang(2003) suggested a way to circumvent the censoring issue by incorporating Kaplan-Meier type weight in halfspace regression depth and Park(2003) used a similar technique to simplicial regression depth. Hubert et al. (2001) suggested a high breakdown point regression depth based on projection called rcent. A new method to implement censoring in rcent is suggested and compared with two precedents under various contamination and censoring schemes.

• Journal of the Korean Statistical Society
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• v.28 no.2
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• pp.195-210
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• 1999

We consider a large class of exponential regression models with censored data and propose two modified Fisher scoring methods with corresponding algorithms. These proposed methods improve the Newton-Raphson method in estimating the model parameters. The simulated and real examples are illustrated in aspect of convergence.

• Journal of Korean Society for Quality Management
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• v.25 no.2
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• pp.47-59
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• 1997

In this paper we develop computational algorithms to calculate M-estimators of regression parameters from right-censored data that are naturally collected in quality control. In the case of M-estimators, a new statistical method is also introduced to incorporate concomitant scale estimation in the presence of right censoring on the observed responses. Furthermore, we illustrate this by simulations.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.219-225
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• 1998

The parameters in linear models with censored normal responses are usually estimated by the iterative maximum likelihood and least square methods. However, the iterative least square method is simple but hardly has theoretical justification, and the iterative maximum likelihood estimating equations are complicatedly derived. In this paper, we justify these methods via Wedderburn (1974)'s quasi-likelihood approach. This provides an explicit justification for the iterative least square method and also directly the iterative maximum likelihood method for estimating the regression coefficients.

• Communications for Statistical Applications and Methods
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• v.21 no.4
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• pp.285-296
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• 2014

The quantile residual life function has been effectively used to interpret results from the analysis of the proportional hazards model for censored survival data; however, the quantile residual life function is not always estimable with currently available semi-parametric regression methods in the presence of heavy censoring. A parametric regression approach may circumvent the difficulty of heavy censoring, but parametric assumptions on a baseline hazard function can cause a potential bias. This article proposes a Bayesian semi-parametric regression approach for inference on an unknown baseline hazard function while adjusting for available covariates. We consider a model-based approach but the proposed method does not suffer from strong parametric assumptions, enjoying a closed-form specification of the parametric regression approach without sacrificing the flexibility of the semi-parametric regression approach. The proposed method is applied to simulated data and heavily censored survival data to estimate various quantile residual lifetimes and adjust for important prognostic factors.

• Journal of Korean Society for Quality Management
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• v.27 no.2
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• pp.112-125
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• 1999

In a robust regression model, it is typically assumed that the errors are normally distributed. However, what if the error distribution is deviated from the normality and the response variables are not completely observable due to censoring? For complete data, Kim and Lai(1998) suggested a new adaptive M-estimator with an asymptotically efficient score function. The adaptive M-estimator is based on using B-splines to estimate the score function and simple cross validation to determine the knots of the B-splines, which are a modified version of Kun( 1992). We herein extend this method to right-censored data and study how well the adaptive M-estimator performs for various error distributions and censoring rates. Some impressive simulation results are shown.

• Communications for Statistical Applications and Methods
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• v.26 no.4
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• pp.371-383
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• 2019

Panel data sets have been developed in various areas, and many recent studies have analyzed panel, or longitudinal data sets. Maximum likelihood (ML) may be the most common statistical method for analyzing panel data models; however, the inference based on the ML estimate will have an inflated Type I error because the ML method tends to give a downwardly biased estimate of variance components when the sample size is small. The under estimation could be severe when data is incomplete. This paper proposes the restricted maximum likelihood (REML) method for a random effects panel data model with a censored dependent variable. Note that the likelihood function of the model is complex in that it includes a multidimensional integral. Many authors proposed to use integral approximation methods for the computation of likelihood function; however, it is well known that integral approximation methods are inadequate for high dimensional integrals in practice. This paper introduces to use the moments of truncated multivariate normal random vector for the calculation of multidimensional integral. In addition, a proper asymptotic standard error of REML estimate is given.

• Communications for Statistical Applications and Methods
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• v.25 no.5
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• pp.523-544
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• 2018

Bivariate distributions play a fundamental role in survival and reliability studies. We consider a regression model for bivariate survival times under right-censored based on the bivariate Kumaraswamy Weibull (Cordeiro et al., Journal of the Franklin Institute, 347, 1399-1429, 2010) distribution to model the dependence of bivariate survival data. We describe some structural properties of the marginal distributions. The method of maximum likelihood and a Bayesian procedure are adopted to estimate the model parameters. We use diagnostic measures based on the local influence and Bayesian case influence diagnostics to detect influential observations in the new model. We also show that the estimates in the bivariate Kumaraswamy Weibull regression model are robust to deal with the presence of outliers in the data. In addition, we use some measures of goodness-of-fit to evaluate the bivariate Kumaraswamy Weibull regression model. The methodology is illustrated by means of a real lifetime data set for kidney patients.

• Communications for Statistical Applications and Methods
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• v.23 no.4
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• pp.343-353
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• 2016

In doubly-censored data, an originating event time and a terminating event time are interval-censored. In certain analyses of such data, a researcher might be interested in the elapsed time between the originating and terminating events as well as regression modeling with risk factors. Therefore, in this study, we introduce a model evaluation method to measure the predictive ability of a model based on negative predictive values. We use a semiparametric estimate of the predictive accuracy to provide a simple and flexible method for model evaluation of doubly-censored survival outcomes. Additionally, we used simulation studies and tested data from a prostate cancer trial to illustrate the practical advantages of our approach. We believe that this method could be widely used to build prediction models or nomograms.

• Communications for Statistical Applications and Methods
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• v.15 no.2
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• pp.161-171
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• 2008

This study discusses Johnson's $S_U$-normal distribution capturing a wide range of non-normality in various regression models. We provide the likelihood inference using Johnson's $S_U$-normal distribution, and propose a likelihood ratio (LR) test for normality. We also apply the $S_U$-normal distribution to the binary and censored regression models. Monte Carlo simulations are used to show that the LR test using the $S_U$-normal distribution can be served as a model specification test for normal error distribution, and that the $S_U$-normal maximum likelihood (ML) estimators tend to yield more reliable marginal effect estimates in the binary and censored model when the error distributions are non-normal.