• 한국음향학회지
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• 제16권5호
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• pp.83-91
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• 1997

원자로의 구조적 건전성을 확보하고 사고를 미연에 방지하기 위해서 중성자 잡음 신호를 이용한 진동 감시에는 주기성 도표(periodogram), 평균주기성도표(averaged periodobram), Blackman-Tukey 스펙트럼 추정 등을 이용하고 있으나 본 논문에서는 통계적인 비편향성(unbaised), 일치성(consistency), 효율성(efficiency), 충족성(minimum lower bound)을 고려한 파라미터 모델링 방법 중 AR 모델을 이용하여 원자로 구조물의 최적의 파라미터를 추정하고 진동 감시에 필요한 스펙트럼 분석의 해상도를 높였다. 특히 논문에서는 차수 선정에서 AR 모델의 적절한 차수선정(order selection)을 위하여 자기상관의 lag value을 이용하였다. AR 방법중 Burg 방법이 원자로 구조물의 고유진동수를 추적하는데 가장 효과적이다.

• Journal of the Korean Statistical Society
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• 제26권4호
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• pp.543-554
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• 1997

A family of some capability indices { $C_{p}$(.alpha.,.beta.); .alpha..geq.0, .beta..geq.0}, containing the indices $C_{p}$, $C_{{pk}}$, $C_{{pm}}$, and $C_{{pmk}}$, has been defined by Vannman(1993) for the case of two-sided specification interval. By varying the parameters of the family various capability indices with suitable properties are obtained. We derive tha asymptotic distribution of the family { $C_{p}$(.alpha.,.beta.); .alpha..geq.0,.beta..geq.0} under general proper conditions. It is also shown that the bootstrap approximation to the distribution of the estimator $C_{p}$(.alpha., .beta.) is vaild for almost all sample sequences. These asymptotic distributions would be used in constructing some bootstrap confidence intervals.tervals.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2000년도 추계학술발표회 논문집
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• pp.23-26
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• 2000

It is noted that many econometric time series have long-memory properties. A long-memory process, or strongly dependent process, is characterized by hyperbolic decaying autocorrelations and unbounded spectral density at the origin. Since the long-memory property can be observed by data obtained from rather a long period, there is some possibility of parameter change in the process. In this paper, we consider the estimation of change-point when there is a change in the variance of a long-memory process. The estimator is based on some reasonable statistic and the consistency is shown using Taqqu's strong reduction theorem

• Journal of the Korean Statistical Society
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• 제18권1호
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• pp.13-25
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• 1989

This paper suggests a classification rule with unequal covariance matrices when a patterned incomplete data are involved in the discriminant analysis. This is an extension of Geisser's (1966) result to the case of missing observations. For the calssificaiton rule, we introduce an algorithm which contains data augmentation step and Monte Carlo integration step and show that the algorithm yields a consistant estimator of true classification probability. The proposed method is compared to the complete observation vector method through a Monte Carlo study. The results show that the suggested method, in general, performs better than the complete observation vector method which ignores those vectors of observation with one or more missing values from the analysis. The results also verify the consistency of the algorithm.

• Communications for Statistical Applications and Methods
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• 제26권3호
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• pp.273-293
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• 2019

We consider the problem of model selection in multiple linear regression with outliers and non-normal error distributions. In this article, the robust model selection criterion is proposed based on the robust estimation method with the least absolute deviation (LAD). The proposed criterion is shown to be consistent. We suggest proposed criterion based algorithms that are suitable for a large number of predictors in the model. These algorithms select only relevant predictor variables with probability one for large sample sizes. An exhaustive simulation study shows that the criterion performs well. However, the proposed criterion is applied to a real data set to examine its applicability. The simulation results show the proficiency of algorithms in the presence of outliers, non-normal distribution, and multicollinearity.

• Communications for Statistical Applications and Methods
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• 제20권4호
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• pp.259-270
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• 2013

This paper considers a penalized composite quantile regression (CQR) that performs a variable selection in the linear model with grouped variables. An adaptive sup-norm penalized CQR (ASCQR) is proposed to select variables in a grouped manner; in addition, the consistency and oracle property of the resulting estimator are also derived under some regularity conditions. To improve the efficiency of estimation and variable selection, this paper suggests the two-stage penalized CQR (TSCQR), which uses the ASCQR to select relevant groups in the first stage and the adaptive lasso penalized CQR to select important variables in the second stage. Simulation studies are conducted to illustrate the finite sample performance of the proposed methods.

• 응용통계연구
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• 제11권1호
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• pp.151-161
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• 1998

본 논문에서는 Koziol-Green 모형에서 생존함수에 대한 신뢰구간을 붓스트랩 방법을 이용하여 제안하고, 생존함수에 대한 붓스트랩 추정량의 일치성을 밝힌다. 또한 제안된 붓스트랩 신뢰구간들을 기존의 근사적 정규분포를 이용한 신뢰구간과 생존함수에 변수변환을 고려하여 구성한 신뢰구간들과 모의실험을 통하여 비교한 결과 제안된 붓스트랩 신뢰구간이 기존의 방법보다 포함확률 측면에서 더 좋은 결과를 보였고 중도절단율에 덜 민감한다는 것을 보여 주었다.

• Communications for Statistical Applications and Methods
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• 제25권4호
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• pp.341-354
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• 2018

The one of the principles described in ICH E9 is that only results obtained from pre-specified statistical methods in a protocol are regarded as confirmatory evidence. However, in multi-regional clinical trials, even when results obtained from pre-specified statistical methods in protocol are significant, it does not guarantee that the test treatment is approved by regional regulatory agencies. In other words, there is no so-called global approval, and each regional regulatory agency makes its own decision in the face of the same set of data from a multi-regional clinical trial. Under this situation, there are two natural methods a regional regulatory agency can use to estimate the treatment effect in a particular region. The first method is to use the overall treatment estimate, which is to extrapolate the overall result to the region of interest. The second method is to use regional treatment estimate. If the treatment effect is completely identical across all regions, it is obvious that the overall treatment estimator is more efficient than the regional treatment estimator. However, it is not possible to confirm statistically that the treatment effect is completely identical in all regions. Furthermore, some magnitude of regional differences within the range of clinical relevance may naturally exist for various reasons due to, for instance, intrinsic and extrinsic factors. Nevertheless, if the magnitude of regional differences is relatively small, a conventional method to estimate the treatment effect in the region of interest is to extrapolate the overall result to that region. The purpose of this paper is to investigate the effects produced by this type of extrapolation via estimations, followed by hypothesis testing of the treatment effect in the region of interest. This paper is written from the viewpoint of regional regulatory agencies.

• Communications for Statistical Applications and Methods
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• 제19권3호
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• pp.405-414
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• 2012

층화확률추출은 모집단을 어떤 층화기준에 의해 여러 층으로 분할한 다음 각 층으로부터 독립적으로 표본을 임의추출하는 방법으로 여러 가지 장점을 가지고 있어 실제 조사에서 많이 활용되고 있다. 본 연구에서는 대규모 표본조사에서 많이 사용하고 있는 층화확률추출을 사용하여 추출된 표본을 통해 모평균에 대한 붓스트랩 추정량과 신뢰구간 및 가설검정 등 통계적 추론에 대하여 연구하였다. 층화모집단에서의 모평균의 추정량과 관련된 극한 분포이론들을 기초로 붓스트랩 일치성을 근거로 층화 모평균에 대해 표준 붓스트랩 방법, 백분위수 붓스트랩 방법, 스튜던트화 붓스트랩 방법을 활용한 신뢰구간과 붓스트랩 가설검정 방법을 제안하였으며, 모의실험을 통해 신뢰구간 추정 방법들의 유효성을 확인하였다.

• 대한산업공학회지
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• 제21권4호
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• pp.609-628
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• 1995

This paper presents a reliability model and a data-analytic procedure for a repairable unit subject to failures due to multiple non-identifiable causes. We regard a failure cause as a state and assume the life distribution for each cause to be exponential. Then we represent the dependency among the causes by a Markov switching model(MSM) and estimate the transition probabilities and failure rates by maximum likelihood(ML) method. The failure data are incomplete due to masked causes of failures. We propose a specific version of EM(expectation and maximization) algorithm for finding maximum likelihood estimator(MLE) under this situation. We also develop statistical procedures for determining the number of significant states and for testing independency between state transitions. Our model requires only the successive failure times of a unit to perform the statistical analysis. It works well even when the causes of failures are fully masked, which overcomes the major deficiency of competing risk models. It does not require the assumption of stationarity or independency which is essential in mixture models. The stationary probabilities of states can be easily calculated from the transition probabilities estimated in our model, so it covers mixture models in general. The results of simulations show the consistency of estimation and accuracy gradually increasing according to the difference of failure rates and the frequency of transitions among the states.