• Industry Promotion Research
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• v.7 no.1
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• pp.1-8
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• 2022

We investigated various types of machine learning methods that can be applied to censored data. Exploratory data analysis reveals the distribution of each feature, relationships among features. Next, classification problem has been set up where the dependent variable is death_event while the rest of the features are independent variables. After applying various machine learning methods to the data, it has been found that just like many other reports from the artificial intelligence arena random forest performs better than logistic regression. But recently well performed artificial neural network and gradient boost do not perform as expected due to the lack of data. Finally Kaplan-Meier and Cox proportional hazard model have been employed to explore the relationship of the dependent variable (t_i, δ_i) with the independent variables. Also random forest which is used in machine learning has been applied to the survival analysis with censored data.

• Communications for Statistical Applications and Methods
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• v.13 no.1
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• pp.101-111
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• 2006

One of the most widely used method of handling missingness in microarray data is the kNN(k Nearest Neighborhood) method. Recently Li and Gui (2004) suggested, so called PCR(Partial Cox Regression) method which deals with censored survival times and microarray data efficiently via kNN imputation method. In this article, we try to show that the way to treat missingness eventually affects the further statistical analysis.

• Journal of the Korean Data and Information Science Society
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• v.5 no.1
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• pp.59-66
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• 1994

Heller and Simonoff(1990) compared several methods of estimating the regression coefficient in a modified proportional hazards model, when the response variable is subject to censoring. We give another method of estimating the parameters in the model which also allows the dependent variable to be censored and the error distribution to be unspecified. The proposed method differs from that of Miller(1976) and that of Buckely and James(1979). We also obtain the variance estimator of the coefficient estimator and compare that with the Buckely-James Variance estimator studied by Hillis(1993).

• Journal of the Korean Data and Information Science Society
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• v.24 no.2
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• pp.211-222
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• 2013

We consider the problem of nonparametrically estimating the conditional quantile function from censored data and propose new estimators here. They are based on local logistic regression technique of Lee et al. (2006) and "double-kernel" technique of Yu and Jones (1998) respectively, which are modified versions under random censoring. We compare those with two existing estimators based on a local linear fits using the check function approach. The comparison is done by a simulation study.

• Communications for Statistical Applications and Methods
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• v.26 no.2
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• pp.91-102
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• 2019

In this paper, we propose a new estimation method based on a weighted linear regression framework to obtain some estimators for unknown parameters in a two-parameter Rayleigh distribution under a progressive Type-II censoring scheme. We also provide unbiased estimators of the location parameter and scale parameter which have a nuisance parameter, and an estimator based on a pivotal quantity which does not depend on the other parameter. The proposed weighted least square estimator (WLSE) of the location parameter is not dependent on the scale parameter. In addition, the WLSE of the scale parameter is not dependent on the location parameter. The results are compared with the maximum likelihood method and pivot-based estimation method. The assessments and comparisons are done using Monte Carlo simulations and real data analysis. The simulation results show that the estimators ${\hat{\mu}}_u({\hat{\theta}}_p)$ and ${\hat{\theta}}_p({\hat{\mu}}_u)$ are superior to the other estimators in terms of the mean squared error (MSE) and bias.

• The Korean Journal of Applied Statistics
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• v.30 no.4
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• pp.539-553
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• 2017

We propose a multi-state model to analyze semi-competing risks data with interval-censored or missing intermediate events. This model is an extension of the three states of the illness-death model: healthy, disease, and dead. The 'diseased' state can be considered as the intermediate event. Two more states are added into the illness-death model to incorporate the missing events, which are caused by a loss of follow-up before the end of a study. One of them is a state of the lost-to-follow-up (LTF), and the other is an unobservable state that represents an intermediate event experienced after the occurrence of LTF. Given covariates, we employ the Lin and Ying additive hazards model with log-normal frailty and construct a conditional likelihood to estimate transition intensities between states in the multi-state model. A marginalization of the full likelihood is completed using adaptive importance sampling, and the optimal solution of the regression parameters is achieved through an iterative quasi-Newton algorithm. Simulation studies are performed to investigate the finite-sample performance of the proposed estimation method in terms of empirical coverage probability of true regression parameters. Our proposed method is also illustrated with a dataset adapted from Helmer et al. (2001).

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1231-1246
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• 2016

Cause-specific hazard model (Prentice et al., 1978) and subdistribution hazard model (Fine and Gray, 1999) are mostly used for the right censored survival data with competing risks. Some other models for survival data with competing risks have been subsequently introduced; however, those models have not been popularly used because the models cannot provide reliable statistical estimation methods or those are overly difficult to compute. We introduce simple and reliable competing risk regression models which have been recently proposed as well as compare their methodologies. We show how to use SAS and R for the data with competing risks. In addition, we analyze survival data with two competing risks using five different models.

• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1045-1054
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• 2012

Support vector machine is known to be the very useful statistical method in classification and nonlinear function estimation. In this paper we propose a monotone support vector regression (SVR) for the estimation of monotonically decreasing function. The proposed monotone SVR is applied to smooth the Kaplan-Meier estimate of survival function. Experimental results are then presented which indicate the performance of the proposed monotone SVR using survival functions obtained by exponential distribution.

• Journal of the Korean Data and Information Science Society
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• v.21 no.4
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• pp.765-775
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• 2010

For the accelerated failure time (AFT) model a lot of effort has been devoted to develop effective estimation methods. AFT model assumes a linear relationship between the logarithm of event time and covariates. In this paper we propose a semiparametric support vector machine to consider situations where the functional form of the effect of one or more covariates is unknown. The proposed estimating equation can be computed by a quadratic programming and a linear equation. We study the effect of several covariates on a censored response variable with an unknown probability distribution. We also provide a generalized approximate cross-validation method for choosing the hyper-parameters which affect the performance of the proposed approach. The proposed method is evaluated through simulations using the artificial example.

• Management Science and Financial Engineering
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• v.11 no.3
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• pp.125-136
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• 2005

As the importance of time-based competition is increasing, information systems for supporting the immediate decision making is strongly required. Especially high -tech product firms are under extreme pressure of rapid response to the demand side due to relatively short life cycle of the product. Therefore, the objective of our research is proposing a framework of estimating demand curve based on e-auction data, which is extremely easy to access and well reflect the limited demand curve in that channel. Firstly, we identify the advantages of using e-auction data for full demand curve estimation and then verify it using Agent-Eased-Modeling and Tobin's censored regression model.