• 한국멀티미디어학회논문지
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• 제8권6호
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• pp.783-789
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• 2005

This paper is proposed a watermarking technique for copyright protection of multimedia contents. We proposed adaptive watermark detection algorithm using stochastic perceptual model and statistical decision method in DMWT(discrete multi wavelet transform) domain. The stochastic perceptual model calculates NVF(noise visibility function) based on statistical characteristic in the DMWT. Watermark detection algorithm used the likelihood ratio depend on Bayes' decision theory by reliable detection measure and Neyman-Pearson criterion. To reduce visual artifact of image, in this paper, adaptively decide the embedding number of watermark based on DMWT, and then the watermark embedding strength differently at edge and texture region and flat region embedded when watermark embedding minimize distortion of image. In experiment results, the proposed statistical decision method based on multiwavelet domain could decide watermark detection.

• Communications for Statistical Applications and Methods
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• 제7권2호
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• pp.605-616
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• 2000

Three primary interests frequently raised by mortgage companies are introduced and the corresponding statistical approaches for the default probability in mortgage companies are examined. Statistical models considered in this paper are time series, logistic regression, decision tree, neural network, and discrete time models. Usage of the models is illustrated using an artificially modified data set and the corresponding models are evaluated in appropriate manners.

• 한국통신학회논문지
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• 제22권7호
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• pp.1411-1418
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• 1997

This paper presents a statistical analysis of the decision throeshold for derect-sequence spread-spectrum (DS-SS) prallel Pseudo-Noise (PN) code acquistion with a reference filter. The probabilities of detection and false alarm are derived, and the mean acquistion time is evaluated as a measure of the system performance in both nonfading and Rician fiding channels. From the statistical sresults, it is shown that in the performance analysis of the parallel acquisition system with reference filtering, the statistical evaluaion of the decision threshold seems more appropriate than the approximation of the decision threshold adopted in the other schemes[2,3].

• 응용통계연구
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• 제10권2호
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• pp.385-401
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• 1997

연속적인 생산공정에서 꾸준하면서도 작은 품질의 변화를 신속하게 탐지하는 통계적 절차로서 누적합(CUSUM) 관리도를 많이 사용하고 있다. 본 논문에서는 누적합 관리도의 평균런길이를 근사하는 방법과 누적합관리도의 통계적 설계, 즉 관리상태에서의 평균런길이가 일정한 값으로 고정되었을 경우 이를 만족하는 결정구간을 설정하는 방법을 제시한다. 또한 이 방법을 관측값이 정규분포와 지수분포를 따르는 경우에 적용시켜 그 정확성을 비교하고 있다.

• Communications for Statistical Applications and Methods
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• 제16권6호
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• pp.959-969
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• 2009

결정나무는 데이터마이닝에서 데이터를 분류하는 기법으로 가장 많이 사용되고 있으며, 데이터 탐색 소프트웨어 DAVIS에서는 동적 기능을 사용하여 데이터 시각화를 하는 것이 가능하다. 본 논문에서는 동적 데이터 분석의 기본 원리와 이를 결정나무에 적용하는 방법을 소개하고, 생성되는 동적 결정나무의 효율성과 유용성을 실제 데이터를 사용하여 분석한다.

• Communications for Statistical Applications and Methods
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• 제8권3호
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• pp.823-829
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• 2001

Even though a rapid development of modem medical science, paralysis disease is a highly dangerous and murderous disease. Shin et al. (1978) constructed the diagnosis expert system which identify a type of the paralysis disease from symptoms of a paralysis disease patients by using the canonical discriminant analysis. The decision tree-based analysis, however, has advantages over the method used in Shin et al. (1998), such as it does not need assumptions - linearity and normality, and suggest appropriate diagnosis procedure which is easily explained. In this paper, we applied the decision tree to construct the model which Identify a type of the paralysis disease.

• 대한산업공학회지
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• 제15권2호
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• pp.33-44
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• 1989

This paper presents stochastic-statistical dominance rules which eliminate dominated alternatives thereby reduce the number of satisficing alternatives to a manageable size so that the decision maker can choose the best alternative among them when neither the utility function nor the probability distribution of outcomes is exactly known. Specifically, it is assumed that only the characteristics of the utility function and the value function are known. Also, it is assumed that prior probabilities of the mutually exclusive states of nature are not known, but their relative bounds are known. First, the notion of relative risk aversion is used to describe the decision maker's attitude toward risk, which is defined with the acknowledgement that the utility function of the decision maker is a composite function of a cardinal value function and a utility function with-respect to the value function. Then, stochastic-statistical dominance rules are developed to screen out dominated alternatives according to the decision maker's attitude toward risk represented in the form of the measure of relative risk aversion.

• Communications for Statistical Applications and Methods
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• 제24권6호
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• pp.543-559
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• 2017

Tree-based regression and classification ensembles form a standard part of the data-science toolkit. Many commonly used methods take an algorithmic view, proposing greedy methods for constructing decision trees; examples include the classification and regression trees algorithm, boosted decision trees, and random forests. Recent history has seen a surge of interest in Bayesian techniques for constructing decision tree ensembles, with these methods frequently outperforming their algorithmic counterparts. The goal of this article is to survey the landscape surrounding Bayesian decision tree methods, and to discuss recent modeling and computational developments. We provide connections between Bayesian tree-based methods and existing machine learning techniques, and outline several recent theoretical developments establishing frequentist consistency and rates of convergence for the posterior distribution. The methodology we present is applicable for a wide variety of statistical tasks including regression, classification, modeling of count data, and many others. We illustrate the methodology on both simulated and real datasets.

• Nuclear Engineering and Technology
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• 제54권1호
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• pp.414-422
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• 2022

Presented in this paper is a methodology for combining a Bayesian statistical approach with Data Quality Objectives (a structured decision-making method) to provide increased levels of confidence in analytical data when approaching a waste boundary. Development of sampling and analysis plans for the characterisation of radioactive waste often use a simple, one pass statistical approach as underpinning for the sampling schedule. Using a Bayesian statistical approach introduces the concept of Prior information giving an adaptive sample strategy based on previous knowledge. This aligns more closely with the iterative approach demanded of the most commonly used structured decision-making tool in this area (Data Quality Objectives) and the potential to provide a more fully underpinned justification than the more traditional statistical approach. The approach described has been developed in a UK regulatory context but is translated to a waste stream from the Fukushima Daiichi Nuclear Power Station to demonstrate how the methodology can be applied in this context to support decision making regarding the ultimate disposal option for radioactive waste in a more global context.

• Journal of the Korean Statistical Society
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• 제8권1호
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• pp.15-21
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• 1979

A zone of sensitivity is developed to investigate the effect of nonnormality on the Bayes decision function for testing mean of a normal population when either parent or prior belongs to Edgeworthian family of moderately nonnormal probability density functions.