• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1573-1582
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• 2015

In lifetime data analysis, it is generally known that the lifetimes of test items may not be recorded exactly. There are also situations wherein the withdrawal of items prior to failure is prearranged in order to decrease the time or cost associated with experience. Moreover, it is generally known that more than one cause or risk factor may be present at the same time. Therefore, analysis of censored competing risks data are needed. In this article, we derive the Bayes estimators for the entropy function under the exponential distribution with an unknown scale parameter based on multiply Type II censored competing risks data. The Bayes estimators of entropy function for the exponential distribution with multiply Type II censored competing risks data under the squared error loss function (SELF), precautionary loss function (PLF) and DeGroot loss function (DLF) are provided. Lindley's approximate method is used to compute these estimators.We compare the proposed Bayes estimators in the sense of the mean squared error (MSE) for various multiply Type II censored competing risks data. Finally, a real data set has been analyzed for illustrative purposes.

• Communications for Statistical Applications and Methods
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• v.30 no.4
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• pp.389-402
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• 2023

Multivariate or clustered failure time data often occur in many medical, epidemiological, and socio-economic studies when survival data are collected from several research centers. If the data are periodically observed as in a longitudinal study, survival times are often subject to various types of interval-censoring, creating multivariate interval-censored data. Then, the event times of interest may be correlated among individuals who come from the same cluster. In this article, we propose a unified linear regression method for analyzing multivariate interval-censored data. We consider a semiparametric multivariate accelerated failure time model as a statistical analysis tool and develop a generalized Buckley-James method to make inferences by imputing interval-censored observations with their conditional mean values. Since the study population consists of several heterogeneous clusters, where the subjects in the same cluster may be related, we propose a generalized estimating equations approach to accommodate potential dependence in clusters. Our simulation results confirm that the proposed estimator is robust to misspecification of working covariance matrix and statistical efficiency can increase when the working covariance structure is close to the truth. The proposed method is applied to the dataset from a diabetic retinopathy study.

• Journal of Fisheries and Marine Sciences Education
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• v.27 no.6
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• pp.1755-1763
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• 2015

The objective of this study is to empirically analyze the relationship between sea temperature and jellyfish blooms. Ever since the 2000s, jellyfish population has been dramatically increased, which brought negative influence on the national health and the fisheries activities. Jellyfish blooms have been recognized as an effect of climate change, but there has been no empirical evidence to support such relationship. In this paper, the relationship between sea temperature and jellyfish blooms has been analyzed by using the regional jellyfish monitoring data and coastal stationary observing data of National Institute of Fisheries Science. Since the dependant variable carries left censoring issues, we used the panel tobit model. Our results indicate that there are statistically significant positive relationship between sea temperature and jellyfish blooms.

• Journal of the Korean Data and Information Science Society
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• v.4
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• pp.65-74
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• 1993

Suppose that we have two populations(or systems), say ${\Pi}_{1}\;and\;{\Pi}_{2}$, to be tested. A random sample of size n from each population is taken and the test for each system will be terminated when the first r failures among n random samples are observed. This kind of test is caned the type-II censored (or item-censored) testing without replacement. Under this scheme we consider the problem of estimating the unknown parameters of interests and the reliability for a given time t for each population.

• Journal of the Korean Data and Information Science Society
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• v.7 no.1
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• pp.21-35
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• 1996

In this paper, estimation and prediction procedures are discussed for grneral situation in which the failure time follows the independent density $f_{i}({\varepsilon}_{i})$ for the accelerated life testing under Type II censoring. In the context of accelerated life test experiment, procedures are given for estimating the parameters in the Eyring model, and for estimating mean life at a given future stress level. The procedures given are conditional confidence interval procedures, obtained by conditioning on ancillary statistics. A comparison is made of these procedures and procedures based on asymptotic properties of the maximum, likelihood estimates.

• Journal of the Korean Data and Information Science Society
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• v.13 no.2
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• pp.87-95
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• 2002

In this paper we consider optimal designs of partially accelerated life testing which is devised for multi-component mixed systems with the considerably long lifetime. Test items are run at both use condition and accelerated condition until a specified censoring time. The optimal criterion for the sample-proportion allocated to accelerated condition is to minimize asymptotic variance of the maximum likelihood estimators of the acceleration factor and hazard rates.

• Journal of the Korean Statistical Society
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• v.32 no.2
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• pp.163-174
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• 2003

We propose a test for independence of bivariate censored data under univariate censorship. To do this, we first introduce a process defined by the difference between bivariate survival function estimator proposed by Lin and Ying (1993) and the product of the product-limit estimators (Kaplan and Meier, 1958) for the marginal survival functions, and derive its asymptotic properties under the null hypothesis of independence. We propose a Cramer-von Mises-type test procedure based on the process . We conduct simulation studies to investigate the finite-sample performance of the proposed test and illustrate the proposed test with a real example.

• The Korean Journal of Applied Statistics
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• v.2 no.2
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• pp.61-78
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• 1989

We consider the step stress life testing which has been developed in order to perform the life testing of the units whose normal life time is long within a reasonable amount of time. The models suggested for statistical analysis of the data obtained form the stress life testing are reviewed and a model which contains these models in some respect is suggested. The statistical inference based on the suggested model is done using maximum likelihood and weighted least square estimates. Finally we review the design of the simple step stress life testing and extend the result to the censoring case.

• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.1081-1091
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• 2007

In this paper, we derive the approximate maximum likelihood estimators of the scale parameter and location parameter in an exponentiated logistic distribution based on multiply Type-II censored samples. We compare the proposed estimators in the sense of the mean squared error for various censored samples. We also propose and compare the estimators of the reliability function by using the proposed estimators of the parameters.

• Communications for Statistical Applications and Methods
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• v.24 no.1
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• pp.43-65
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• 2017

We propose a flexible cure rate survival model by assuming that the number of competing causes of the event of interest has the Poisson distribution and the time for the event follows the gamma-G family of distributions. The extended family of gamma-G failure-time models with long-term survivors is flexible enough to include many commonly used failure-time distributions as special cases. We consider a frequentist analysis for parameter estimation and derive appropriate matrices to assess local influence on the parameters. Further, various simulations are performed for different parameter settings, sample sizes and censoring percentages. We illustrate the performance of the proposed regression model by means of a data set from the medical area (gastric cancer).