• 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.

• 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 Data and Information Science Society
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• v.25 no.6
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• pp.1591-1598
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• 2014

The reviews of the balance sheet of commercial banks showed that loan item constitutes the largest portion of bank's assets. Although the sector has highest rate of profit, it possesses the greatest risk. Identifying factors that can contribute in lifting-up the loan repayment rate of customers of Hawassa district commercial bank is the major goal of this study. A sample of 183 customers who took loan from October, 2005 to April, 2012 was taken from the bank record. Kaplan-Meier estimation method and univariate Cox proportional hazard model were applied to identify factors affecting bank loan repayment rate. The result from Kaplan-Meier survival estimation revealed that the loan repayment rate is significantly related with loan type, and previous loan experience, educational level and mode of repayment. The log-rank test indicates that the survival probability of loan customers is not statistically different in repaying the loan among groups classified by sex. Moreover, the univariate Cox proportional hazard model result portrayed that educational level, having previous loan experience, mode of repayment, collateral type and purpose of loan are significantly related with loan repayment rate of customers commercial bank. Hence, banks should design loan strategies giving special emphasis on the significant factors while they are giving loans to their customers.

• Journal of the Korean Data and Information Science Society
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• v.11 no.2
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• pp.225-233
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• 2000

In life-testing experiments, in which the longest time an experimental unit is on test is not a failure time, but rather a censored observation. For the situation the Kaplan-Meier estimator is known to be a baised estimator of the survival function. Several modifications of the Kaplan-Meier estimator are examined and compared with bias and mean squared error.

• 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 Korea Society of Industrial Information Systems
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• v.20 no.6
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• pp.67-78
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• 2015

The objective of this paper is to analyze survival rate and factors of FDI(Foreign direct investment) using FDI data of Ministry of Knowledge and Economy. Kaplan-Meier estimation was used. The result was as follows. M&A of FDI was much more risk than Greenfield FDI. .FDI to the IT-service industry was much more risk than FDI to the manufacturing industry. Partnership under 50% was much more risk than partnership over 50%. The accumulated survival rate of M&A was higher then Greenfield until fourth period but was lower than Greenfield after fourth period. The accumulated survival rate of M&A was lower than others from the first period to last period. There was no difference between Partnership under 50% and partnership over 50% to 4th period. After 4th period, Accumulated survival rate of partnership under 50% was higher than accumulated survival partnership over 50%.

• International Journal of Reliability and Applications
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• v.9 no.1
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• pp.53-70
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• 2008

In this paper, we discuss the plethora of uses for the software package R, and focus specifically on its helpful applications in reliability data analyses. Examples are presented; including the R coding protocol, R code, and plots for various statistical as well as reliability analyses. We explore Kaplan-Meier estimates and maximum likelihood estimation for distributions including the Weibull. Finally, we discuss future applications of R, and usages of quantile regression in reliability.

• Communications for Statistical Applications and Methods
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• v.25 no.6
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• pp.647-658
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• 2018

In this paper, we study statistical inferences on the maximum likelihood estimation of a normal distribution when data are randomly censored. Likelihood equations are derived assuming that the censoring distribution does not involve any parameters of interest. The maximum likelihood estimators (MLEs) of the censored normal distribution do not have an explicit form, and it should be solved in an iterative way. We consider a simple method to derive an explicit form of the approximate MLEs with no iterations by expanding the nonlinear parts of the likelihood equations in Taylor series around some suitable points. The points are closely related to Kaplan-Meier estimators. By using the same method, the observed Fisher information is also approximated to obtain asymptotic variances of the estimators. An illustrative example is presented, and a simulation study is conducted to compare the performances of the estimators. In addition to their explicit form, the approximate MLEs are as efficient as the MLEs in terms of variances.

• Communications for Statistical Applications and Methods
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• v.24 no.5
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• pp.519-531
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• 2017

We consider goodness-of-fit test statistics for Weibull distributions when data are randomly censored and the parameters are unknown. Koziol and Green (Biometrika, 63, 465-474, 1976) proposed the $Cram\acute{e}r$-von Mises statistic's randomly censored version for a simple hypothesis based on the Kaplan-Meier product limit of the distribution function. We apply their idea to the other statistics based on the empirical distribution function such as the Kolmogorov-Smirnov and Liao and Shimokawa (Journal of Statistical Computation and Simulation, 64, 23-48, 1999) statistics. The latter is a hybrid of the Kolmogorov-Smirnov, $Cram\acute{e}r$-von Mises, and Anderson-Darling statistics. These statistics as well as the Koziol-Green statistic are considered as test statistics for randomly censored Weibull distributions with estimated parameters. The null distributions depend on the estimation method since the test statistics are not distribution free when the parameters are estimated. Maximum likelihood estimation and the graphical plotting method with the least squares are considered for parameter estimation. A simulation study enables the Liao-Shimokawa statistic to show a relatively high power in many alternatives; however, the null distribution heavily depends on the parameter estimation. Meanwhile, the Koziol-Green statistic provides moderate power and the null distribution does not significantly change upon the parameter estimation.

• The Mathematical Education
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• v.31 no.2
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• pp.133-140
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• 1992

The problem of estimating a smooth distribution function F at a point $\tau$ based on randomly right censored data is treated under certain smoothness conditions on F . The asymptotic performance of a certain class of kernel estimators is compared to that of the Kap lan-Meier estimator of F($\tau$). It is shown that the .elative deficiency of the Kaplan-Meier estimate. of F($\tau$) with respect to the appropriately chosen kernel type estimate. tends to infinity as the sample size n increases to infinity. Strong uniform consistency and the weak convergence of the normalized process are also proved.