• 한국전문물리치료학회지
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• 제23권2호
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• pp.93-99
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• 2016

Background: Parametric statistical procedures are typically conducted under the condition in which a sample distribution is statistically identical with its population. In reality, investigators use inferential statistics to estimate parameters based on the sample drawn because population distributions are unknown. The uncertainty of limited data from the sample such as lack of sample size may be a challenge in most rehabilitation studies. Objects: The purpose of this study is to review the bootstrapping method to overcome shortcomings of limited sample size in rehabilitation studies. Methods: Articles were reviewed. Results: Bootstrapping method is a statistical procedure that permits the iterative re-sampling with replacement from a sample when the population distribution is unknown. This statistical procedure is to enhance the representativeness of the population being studied and to determine estimates of the parameters when sample size are too limited to generalize the study outcome to target population. The bootstrapping method would overcome limitations such as type II error resulting from small sample sizes. An application on a typical data of a study represented how to deal with challenges of estimating a parameter from small sample size and enhance the uncertainty with optimal confidence intervals and levels. Conclusion: Bootstrapping method may be an effective statistical procedure reducing the standard error of population parameters under the condition requiring both acceptable confidence intervals and confidence level (i.e., p=.05).

• 한국음향학회지
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• 제26권5호
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• pp.194-198
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• 2007

본 논문에서는 음성의 통계적 모델에 기반한 음성검출기의 성능향상을 위해 변별적 가중치 학습(discriminative weight training) 기반의 최적화된 우도비 테스트(Likelihood Ratio Test, LRT)를 제안한다. 먼저, 기존의 통계모델기반의 음성검출기를 분석하고, 이를 기반으로 MCE(minimum classification error)방법을 도입하여, 각 주파수 채널별로 다른 가중치를 가지는 우도비 기반의 음성검출 결정법(decision rule)을 제시한다. 제안된 알고리즘은 비정상(non-stationary)잡음환경에서 기존의 동일 가중치를 가지는 기하 평균 기반의 음성검출기와 비교하였으며, 우수한 성능을 보인다.

• Communications for Statistical Applications and Methods
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• 제15권5호
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• pp.737-752
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• 2008

오차에 대한 분석은 18세기 천문학과 측지학에서 시작된 뒤, 19세기 초 가우스와 라플라스에 의해 정규분포 및 최소제곱법과 결합되면서 오차이론이라고 불리기 시작하였다. 19세기 중엽 벨기에의 케틀레는 자연과학의 관측결과를 분석하는데 쓰이던 오차이론을 사회 데이터에 적용함으로써 사회 연구를 보다 더 과학적인 연구로 만들어보려 하였다. 그는 사회데이터에서 개인의 특수성을 배제하고 집단의 보편적인 사실만을 나타내는 '평균적인 사람'이라는 개념을 만들었다. 또 그는 비슷한 조건에 있는 여러 사람을 측정한 결과는 단일한 대상을 반복측정한 결과와 마찬가지라고 보고, 천문학의 오차이론을 사회데이터에 적용하였다. 이 논문에서는 오차와 정규분포가 사회 연구에 도입되면서 새로이 나타난 개인과 집단의 관계를 비롯하여 오차이론에 대한 반대 의견들, 오차를 대신하여 나타난 용어 등을 중심으로 19세기 중반에 통계학의 영역이 확대되는 과정을 살펴보았다.

• 산업공학
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• 제17권1호
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• pp.1-12
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• 2004

In a modern semiconductor device manufacturing industry, statistical bin limits on wafer level test bin data are used for minimizing value added to defective product as well as protecting end customers from potential quality and reliability excursion. Most wafer level test bin data show skewed distributions. By Monte Carlo simulation, this paper evaluates methods and sample size effect regarding determination of statistical bin limits. In the simulation, it is assumed that wafer level test bin data follow the Poisson distribution. Hence, typical shapes of the data distribution can be specified in terms of the distribution's parameter. This study examines three different methods; 1) percentile based methodology; 2) data transformation; and 3) Poisson model fitting. The mean square error is adopted as a performance measure for each simulation scenario. Then, a case study is presented. Results show that the percentile and transformation based methods give more stable statistical bin limits associated with the real dataset. However, with highly skewed distributions, the transformation based method should be used with caution in determining statistical bin limits. When the data are well fitted to a certain probability distribution, the model fitting approach can be used in the determination. As for the sample size effect, the mean square error seems to reduce exponentially according to the sample size.

• 한국한의학연구원논문집
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• 제14권2호
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• pp.87-91
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• 2008

Background and Purpose: The purpose of this study was investigate statistical validities of previously reported articles that used various statistical techniques such as t-test and analysis of variance. Methods: To analyze the statistical procedures, 66 original articles using those statistical methods were selected from "Korean Journal of Oriental Medicine(KJOM)" published from 1995 to 2007. Results: Twenty-one articles(32%) did not report correct p-values, 33 articles(50%) used mean${\pm}$standard error(mean${\pm}$SE) and 11 articles(l7%) used mean${\pm}$standard deviation(mean${\pm}$SD). Fifty-two articles(95%) of 55 ones which were tested for normal distribution made an error in describing normal distribution. Seventeen articles misused t-test and 12 articles did not carry out the multiple comparison. Conclusions: The training of researchers with clinical statistics or the participation of statisticians in research design will reduce the significant errors in statistical interpretation of the results.

• 산업경영시스템학회지
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• 제23권61호
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• pp.89-98
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• 2000

For statistical process control, the process data are collected by the measurement system. But, the measurement system may have instrument error or/and operator error. In the measured values of products, the total observed variance consists of process variance and variance due to error of measurement system. In this paper, we design more practical T-s control chart considering estimated measurement error The effects of measurement error on the expected total cost and design parameters are investigated.

• Journal of the Korean Data and Information Science Society
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• 제11권2호
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• pp.335-345
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• 2000

Most context use the classical norms on function spaces as the error criteria. Since these norms are all based on the vertical distances between the curves, these can be quite inappropriate from a visual notion of distance. Visual errors in Marron and Tsybakov(1995) correspond more closely to "what the eye sees". Simulation is performed to compare the performance of the regression smoothers in view of MISE and the visual error. It shows that the visual error can be used as a possible candidate of error criteria in the kernel regression estimation.

• Communications for Statistical Applications and Methods
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• 제15권2호
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• pp.255-264
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• 2008

공변량 값이 주어졌을 때 반응변수의 값을 예측하는 데에는 평균제곱오차를 최소로 하는 것을 고려하는 것이 보통이지만, 최근 Park과 Shin (2005), Jones 등 (2007) 등에서 평균제곱오차대신 평균제곱상대오차에 기반한 예측을 연구한바 있다. 이 논문에서는 Jones 등 (2007)의 방법을 대체할 새로운 비모수적 예측법을 제안하고, 제안된 방법의 유효성을 뒷받침하는 간단한 모의실험 결과를 제공한다.

• Communications for Statistical Applications and Methods
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• 제15권5호
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• pp.675-685
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• 2008

This paper studies two sequential procedures to estimate a monotone convex function using $L_2$ monotonization and uniform convexification; one, denoted by FMSC, monotonizes the data first and then, convexifis the monotone estimate; the other, denoted by FCSM, first convexifies the data and then monotonizes the convex estimate. We show that two shape modifiers are not commutable and so does FMSC and FCSM. We compare them numerically in uniform error(UE) and integrated mean squared error(IMSE). The results show that FMSC has smaller uniform error(UE) and integrated mean squared error(IMSE) than those of FCSC.

• Journal of the Korean Statistical Society
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• 제35권2호
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• pp.157-166
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• 2006

This research proposes an efficient Monte Carlo algorithm for computing error probability in high performance digital communication st stems. It characterizes special features of the problem and suggests an importance sampling algorithm specially designed to handle the problem. It uses a shifted exponential density as the importance sampling density, and shows an adaptive way of choosing the rate and the origin of the shifted exponential density. Instead of equal allocation, an intelligent allocation of the samples is proposed so that more samples are allocated to more important part of the error probability. The algorithm uses the nested feature of the error space and avoids redundancy in estimating the probability. The algorithm is applied to an example data set and shows a great improvement in accuracy of the error probability estimation.