Earticle

시뮬레이션 출력분석 방법 중 가장 기본적이며 우수한 결과를 도출 가능한 방법은 독립반복 수행이다. 그러나 시스템이 복잡해질수록 독립 반복 시간 및 비용은 증가한다. 이러한 문제를 해결하기 위해 개발된 시뮬레 이션 출력분석방법 중 하나가 부트스트랩이다. 자기상관이 존재하는 출력 데이터 분석에 적용 가능한 대표적인 방 법으로는 이동블록 부트스트랩, 임계값 부트스트랩, 정상 부트스트랩 방법이 있다. 본 논문에서는 시스템 상태와 상태의 유지시간처럼 동시에 측정되어 이차원으로 표현되는 이변량 데이터에 대한 임계값 부트스트랩 적용 방법 을 제시한다. 특히, 두 변량의 데이터 내부에 각각 자기상관이 존재함은 물론 변량 간의 상관관계도 존재하는 경 우에 대해서 다룬다. 이 경우, 기존의 재추출 방법으로는 올바른 시스템 성능척도의 추론이 불가하다. 본 연구에서 는 이에 대한 해결책으로써 수정된 임계값 부트스트랩을 제시하며 실험을 통해 그 타당성을 검증하였다.

The independent replication is the simplest method for simulation output analysis, and facilitates correct performance evaluation. However, as the system becomes more complex, the runtime and the cost for the method increase. Bootstrap is an alternative method developed to solve the problem. There are representative methods applicable to the autocorrelated data such as moving block bootstrap, stationary bootstrap, and threshold bootstrap. In this paper, a method to apply the threshold bootstrap to bivariate data such as the system state and the state holding time is proposed. In particular, we deal with the case where autocorrelation structure exists within the data of each variate and the correlation between the two variates also exists. In this case, it is impossible to infer correct system performance measures using the existing resampling methods. This study proposes a modified threshold bootstrap as a solution of this case, and perform the coverage test to check its validity through experiments.

This research considers a system which has an ultimate terminal event such as death, critical failure, bankruptcy together with a certain indicative events (temporary malfunction, special treatment, kind of defaults) that frequently occurs before the terminal event comes to the system. Some investigation of a model for the corresponding bivariate data of the system have been done with an explanation of the situation in terms of two continuous variables instead of continuous-discrete variables and some other properties. Also an analysis has been carried out to evaluate the effect of intermediate observation of occurrence of indicative event so that the result can be used for a possible suggestion of an intermediate observing schedule.

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.

In this paper, we obtain the estimator of system reliability for the bivariate Pareto model with bivariate type 1 censored data. We obtain the estimators and approximated confidence intervals of the reliability for the parallel system based on likelihood function and the relative frequency, respectively. Also we present a numerical example by giving a data set which is generated by computer.

In this paper, we obtain the estimator of system reliability for the bivariate Pareto model with bivariate type 1 censored data. We obtain the estimators and approximated confidence intervals of the reliability for the parallel system based on likelihood function and the relative frequency, respectively. Also we present a numerical example by giving a data set which is generated by computer.

In this paper, we consider two components system which the lifetimes follow bivariate pareto model with bivariate random censored data. We assume that the censoring times are independent of the lifetimes of the two components. We develop large sample test for testing independence between two components. Also we present a simulation study which is the test based on asymptotic normal distribution in testing independence.

We consider two components parallel system in which the lifetimes have the bivariate Pareto model with bivariate random censored data. We assume that bivariate Pareto model is affected by common stress which is independent of the lifetimes of the components. We obtain estimators for the system reliability based on likelihood function and relative frequency. Also we construct approximated confidence intervals for the reliability based on maximum likelihood estimator and relative frequency estimator, respectively. Finally we present a numerical study.

In this paper, we consider the Bayesian hypotheses testing for symmetry in Freund's bivariate exponential model with bivariate random censored data. In Bayesian testing problem, we use the noninformative priors for parameters which are improper and are defined only up to arbitrary constants. And we use the recently proposed hypotheses testing criterion called the intrinsic Bayes factor. Also we derive the arithmetic and median intrinsic Bayes factors and give some numerical results to illustrate our results.

In this paper, we assume that strengths of two components system follow a type II bivariate Pareto model with bivariate type I censored data. And these two components are subjected to a common stress which is independent of the strengths of the components. We obtain estimators for the system reliability based on likelihood function and relative frequency, respectively. Also we construct approximated confidence intervals for the reliability based on maximum likelihood estimator and relative frequency estimator, respectively. Finally we present a numerical study.

Recurrent event data frequently occur in clinical studies, demography, engineering reliability and so on (Cook and Lawless, The Statistical Analysis of Recurrent Events, Springer, 2007). Sometimes, two or more different but related type of recurrent events may occur simultaneously. In this study, our interest is to estimate the covariate effect on bivariate recurrent event times with zero inflations. Such zero inflation can be related with susceptibility. In the context of bivariate recurrent event data, furthermore, such susceptibilities may be different according to the type of event. We propose a joint model including both two intensity functions and two cure rate functions. Bivariate frailty effects are adopted to model the correlation between recurrent events. Parameter estimates are obtained by maximizing the likelihood derived under a piecewise constant hazard assumption. According to simulation results, the proposed method brings unbiased estimates while the model ignoring cure rate models gives underestimated covariate effects and overestimated variance estimates. We apply the proposed method to a set of bivariate recurrent infection data in a study of child patients with leukemia.

Subjects can experience two types of recurrent events in a longitudinal study. In addition, there may exist intermittent dropouts that results in repeated observation gaps during which no recurrent events are observed. Therefore, theses periods are regarded as non-risk status. In this paper, we consider a special case where information on the observation gap is incomplete, that is, the termination time of observation gap is not available while the starting time is known. For a statistical inference, incomplete termination time is incorporated in terms of interval-censored data and estimated with two approaches. A shared frailty effect is also employed for the association between two recurrent events. An EM algorithm is applied to recover unknown termination times as well as frailty effect. We apply the suggested method to young drivers' convictions data with several suspensions.

In animal tumorigenicity data, tumor onsets occur at several sites and onset times cannot be exactly observed. Instead, the existence of tumors is examined only at death time or sacrifice time of the animal. Such an incomplete data structure makes it difficult to investigate the effect of treatment on tumor onset times; in addition, such dependence should be considered when censoring due to death is related with tumor onset. A bivariate frailty effect is incorporated to model bivariate tumor onsets and to connect death with tumor. For the inference of parameters, EM algorithm is applied and a real NTP(National Toxicology Program) dataset is analyzed as an illustrative example.

We consider a life testing experiment in which several two-component shared parallel systems are put on test, and the test is terminated at a predesigned experiment time. The bivariate data obtained from such a system-level life testing can be classified into three cases: 1) the case of failed two components with known failures times, 2) the case of censored two components, and 3) the case of one censored component and the other failed component of which the failure time might be known or unknown. In this thesis, the likelihood estimators for Freund's bivariate exponential life distribution under above censoring scheme are obtained. Results of comparative studies based on Monte Carlo simulation are presented.

We consider to obtain an estimate of bivariate survival function for the right censored data with the assumption that the two components of censoring vector are independent. The estimate is derived from an ad hoc approach based on the representation of survival function. Then the resulting estimate can be considered as an extension of the Susarla- Van Ryzin estimate to the bivariate data. Also we show the consistency and weak convergence for the proposed estimate. Finally we compare our estimate with Dabrowska's estimate with an example and discuss some properties of our estimate with brief comment on the extension to the multivariate case.

An estimation problem of treatment effect for bivariate censored survival data is considered under location shift model between two sample. The proposed estimator is very intuitive and can be obtained in a closed form. Asymptotic results of the proposed estimator are discussed and simulation studies are performed to show the strength of the proposed estimator.

Kendall's tau statistic has been applied to test an association of bivariate random variables. However, incomplete bivariate data with a truncation and a censoring results in incomparable or unorderable pairs. With such a partial information, Tsai (1990) suggested a conditional tau statistic and a test procedure for a quasi independence that was extended to more diverse cases such as double truncation and a semi-competing risk data. In this paper, we also employed a conditional tau statistic to estimate an association of bivariate interval censored data. The suggested method shows a better result in simulation studies than Betensky and Finkelstein's multiple imputation method except a case in cases with strong associations. The association of incubation time and infection time from an AIDS cohort study is estimated as a real data example.

This article presents an independence test between pairs of interval censored failure times. The Mantel-Haenszel test is commonly applied to test the independence between two categorical variables accompanied with a strata variable. Hsu and Prentice (1996) applied a Mantel-Haenszel test to the sequence of 2 × 2 tables formed at the grids which are composed of failure times. In this article, due to unknown failure times, the suitable grid points should be determined and the status of failure and at risk are estimated at those grid points. We also consider a weighted test statistic to bring a more powerful test. Simulation studies are performed to evaluate the power of test statistics under finite samples. The method is applied to analyze two real data sets, mastitis data from milk cows and an age-related eye disease study.

In this paper, we consider two-components system which the lifetimes follow bivariate pareto model with censored data. We develop large sample tests for testing independence between two-components. Also we present simulated study which is the test based on asymptotic normal distribution in testing independence.

In this paper, we consider two components system in which the lifetimes follow the bivariate Pareto model with random censored data. We assume that the censoring time is independent of the lifetimes of the two components. We develop large sample tests for testing independence between two components. Also we present simulated study which is the test based on asymptotic normal distribution in testing independence.