• The Korean Journal of Applied Statistics
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• v.13 no.1
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• pp.45-53
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• 2000

본 논문에서는 모든 판정자(judge)들이 모든 객체(object)들에 대해 순위를 부여할 수 없는 경우에 얻어지는 불완전 순위자료에서 판정자들의 처리 효과에 대한 유의성을 검정하는데 관심이 있다. 이를 위해 불완전 순위자료를 완전자료로 바꾸는 알고리즘을 제안하고 알고리즘에 의해 얻어진 완전 순위자료에 Friedman 검정법을 적용하고자 한다. 제안된 검정법은 결측 객체에 순위를 부여하는데 있어서 완전순위를 갖는 판정자들의 정보를 이용함으로서 효율적이며 검정을 시행하는데 기존의 Friedman 통계량에 대한 분포표를 사용할 수 있어 간편하다. 그리고 몬테칼로 모의실험을 통하여 제안된 검정법과 기존의 평균 순위법, 최대/최소 Friedman 검정법과 검정력을 비교하였다.

• Proceedings of the Korean Statistical Society Conference
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• 2002.11a
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• pp.169-174
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• 2002

본 연구는 k개 지수분포 모수들의 기하평균에 대한 베이지안추정 방법을 제시하였다. 이를 위해 Tibshirani가 제안한 직교변환법으로 비정보적 사전확률분포를 도출하여 모수들의 결합사후확률분포를 유도해 내었으며, 이 분포 하에서 가중 몬테칼로 방법을 사용하여 기하평균을 추정하는 절차를 제안하였다. 모의실험과 실제자료의 예를 통해 제안된 베이지안 추정의 유효성 및 효용성을 보였으며, 본 연구에서 제안한 사전확률분포가 전통적인 포함확률을 기준으로 볼 때, Jeffrey의 사전확률분포 보다 더 유효한 추정을 함을 보였다.

• Communications for Statistical Applications and Methods
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• v.4 no.2
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• pp.557-563
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• 1997

Marshall-Olkin의 이변량 지수모형을 따르는 두개의 부품으로 이루어진 시스템에서, 두 부품의 수명들이 이변량 1종 중단된 자료로 관찰되는 경우, 모수에 대한 최우추정량을 구한다. 그리고 두 부품의 수명에 대한 근사적 독립성과 동일성 검정법을 제안하고 몬테칼로 모의실험을 통하여 검정력을 비교하였다.

• Progress in Medical Physics
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• v.16 no.3
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• pp.138-147
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• 2005

The Monte Carlo method cannot have been used for routine treatment planning because of heavy time consumption for the acceptable accuracy. Since calculation time is proportional to particle histories, we can save time by decreasing the number of histories. However, a small number of histories can cause serious uncertainties. In this study, we proposed Monte Carlo dose computation time and uncertainty reduction method using specially designed filters and adaptive denoising process. Proposed algorithm was applied to 6 MV photon and 21 MeV electron dose calculations in homogeneous and heterogeneous phantoms. Filtering time was negligible comparing to Monte Carlo simulation time. The accuracy was improved dramatically in all situations and the simulation of 1 $\%$ to 10$\%$ number of histories of benchmark in photon and electron dose calculation showed the most beneficial result. The empirical reduction of necessary histories was about a factor of ten to fifty from the result.

• The Korean Journal of Applied Statistics
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• v.9 no.2
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• pp.13-23
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• 1996

In this paper, we consider the Marshall and Olkin's bivariate exponential model under random censorship for the distribution of failure times of a system with two components. We propose a bootstrap testing procedure for independence and compare the powers of it with other tests via Monte Carlo simulation.

• The Korean Journal of Applied Statistics
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• v.11 no.1
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• pp.129-139
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• 1998

This article examines the use and some difficulties of Normal Test Variables(NTV) plot for clinical research. Monte Carlo Simulation results are presented based on Normal, Bimodal, Uniform, Exponential and skewed-right distributed Beta Distributions. Further, some solutions are presented and illustrated.

• The Korean Journal of Applied Statistics
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• v.19 no.1
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• pp.97-110
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• 2006

Since it is well-established that even high quality data tend to contain outliers, one would expect fat? greater reliance on robust regression techniques than is actually observed. But most of all robust regression estimators suffers from the computational difficulties and the lower efficiency than the least squares under the normal error model. The weighted self-tuning estimator (WSTE) recently suggested by Lee (2004) has no more computational difficulty and it has the asymptotic normality and the high break-down point simultaneously. Although it has better properties than the other robust estimators, WSTE does not have full efficiency under the normal error model through the weighted least squares which is widely used. This paper introduces a new approach as called the reweighted WSTE (RWSTE), whose scale estimator is adaptively estimated by the self-tuning constant. A Monte Carlo study shows that new approach has better behavior than the general weighted least squares method under the normal model and the large data.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.505-514
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• 2000

A Markov Chain Monte Carlo method with data augmentation is developed to compute the features of the posterior distribution. For each observed failure epoch, we introduced mixture failure model of Rayleigh and Erlang(2) pattern. This data augmentation approach facilitates specification of the transitional measure in the Markov Chain. Gibbs steps are proposed to perform the Bayesian inference of such models. For model determination, we explored sum of relative error criterion that selects the best model. A numerical example with simulated data set is given.

• 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.27 no.6
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• pp.1029-1038
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• 2014

Bayesian experimental design is a useful concept in applied statistics for the design of efficient experiments especially if prior knowledge in the experiment is available. However, a theoretical or numerical approach is not simple to implement. We review the concept of a Bayesian experiment approach for linear and nonlinear statistical models. We investigate relationships between prior knowledge and optimal design to identify Bayesian experimental design process characteristics. A balanced design is important if we do not have prior knowledge; however, prior knowledge is important in design and expert opinions should reflect an efficient analysis. Care should be taken if we set a small sample size with a vague improper prior since both Bayesian design and non-Bayesian design provide incorrect solutions.