• Communications for Statistical Applications and Methods
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• 제8권2호
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• pp.465-471
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• 2001

Randomized response technique is a survey technique for eliminating evasive answer bias. But this technique has a problem. This procedure suffers from linguistic expression in randomizing device. Thus for solving the problem, in this paper, we suggested the randomized response technique using fuzzy logic.

• 한국통신학회논문지
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• 제11권1호
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• pp.73-84
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• 1986

화상이해의 기본적인 특성중의 하나인 edge를 추정하는 statistical 한 방법을 제안하였다. 종래의 edge검출기법은 주로 deterministic한 신호에는 잘 적용되었지만 statistical한 신호에는 만족스러운 결과를 얻을 수 없었다. 본 논문에서는 신호의 statistical 한 성질을 고려한 likelihood함수를 이용하여 결정함수를 구하고, 이것을 최대로 하는 위치를 edge로 선정하는 maximum likelihood edge 검출기법에 대하여 논하였다. 이 기법을 random number generator에 의하여 발생된 통계적인 성질을 갖는 신호에 적용하여 edge가 잘 검출됨을 보였다. 또 이 방법을 통계적인 성질을 갖는 이차원의 화상으로 확장하였을 때에도 정확하게 edge가 검출됨을 알 수 있었다.

• Communications for Statistical Applications and Methods
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• 제10권2호
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• pp.319-324
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• 2003

In this paper, we review three cards method by Droitcour et al.(2001). And we compare indirect survey methods - randomized response technique, item count method, nominative technique.

• Communications for Statistical Applications and Methods
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• 제13권2호
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• pp.319-326
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• 2006

Randomized response technique is a survey technique for eliminating evasive answer bias. This technique is popular in social survey for sensitive issues. In this paper we present a simple and obvious procedure for estimating the population proportion of a sensitive group. Here, we shows the weak point in the method of Kim and Warde (2005). Also, it is found that the proposed procedure is more efficient than the ones of Warner (1965) and Kim and Warde (2005). Lastly we discuss the conditions that the suggested method will be more efficienct.

• Communications for Statistical Applications and Methods
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• 제12권2호
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• pp.509-520
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• 2005

Data fusion is defined as the process of combining data and information from different sources for the effectiveness of the usage of useful information contents. In this paper, we propose a data fusion algorithm using k-means clustering method for data enrichment to improve data quality in knowledge discovery in database(KDD) process. An empirical study was conducted to compare the proposed data fusion technique with the existing techniques and shows that the newly proposed clustering data fusion technique has low MSE in continuous fusion variables.

• Communications for Statistical Applications and Methods
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• 제4권2호
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• pp.445-452
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• 1997

In this paper, twicing technique for the improvement of asymptotic boundary bias in nonparametric regression is considered. Asymptotic mean squared errors of the nonparametric regression estimators are derived at the boundary region by twicing the Nadaraya-Waston and local linear smoothing. Asymptotic biases of the resulting estimators are of order$h^2$and$h^4$ respectively.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1990년도 하계학술대회 논문집
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• pp.72-76
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• 1990

A new statistical linearization technique for nonlinear system called covariance matching method is proposed in this paper. The covariance matching method makes the mean and variance of an approximated output be identical real functional output, and the distribution of the approximated output have identical shape with a given random input. Also, the covariance matching method can be easily implemented for statistical analysis of nonlinear systems with a combination of linear system covariance analysis.

• 한국농공학회논문집
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• 제65권1호
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• pp.1-13
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• 2023

Various techniques for bias correction and statistical downscaling have been developed to overcome the limitations related to the spatial and temporal resolution and error of climate change scenario data required in various applied research fields including agriculture and water resources. In this study, the characteristics of three different statistical dowscaling methods (i.e., SQM, SDQDM, and BCSA) provided by AIMS were summarized, and climate change scenarios produced by applying each method were comparatively evaluated. In order to compare the average rainfall characteristics of the past period, an index representing the average rainfall characteristics was used, and the reproducibility of extreme weather conditions was evaluated through the abnormal climate-related index. The reproducibility comparison of spatial distribution and variability was compared through variogram and pattern identification of spatial distribution using the average value of the index of the past period. For temporal reproducibility comparison, the raw data and each detailing technique were compared using the transition probability. The results of the study are presented by quantitatively evaluating the strengths and weaknesses of each method. Through comparison of statistical techniques, we expect that the strengths and weaknesses of each detailing technique can be represented, and the most appropriate statistical detailing technique can be advised for the relevant research.

• Communications for Statistical Applications and Methods
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• 제28권5호
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• pp.493-510
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• 2021

The present article endeavours to develop partial optional randomized - response technique (PORT) to deal with sensitive issues in presence of non-response in successive sampling. Calibration techniques have been embedded with PORT to estimate sensitive population mean at current move in two move successive sampling in presence of non-response. Optimum calibration weights are computed at each move with the aid of constraints based on auxiliary information. Detailed properties of the proposed estimators have been discussed. Possible cases in which non-response may creep at two moves has been explored. The proposed technique has been compared with the modified existing technique. Simulation results indicate that the proposed technique is more efficient than existing, modified one. Suitable recommendations are forwarded.

• 산업융합연구
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• 제21권1호
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• pp.159-165
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• 2023

최근 지진 발생 빈도가 증가하고 있는 반면 국내 지진 대응 체계는 취약한 현실에서, 본 연구의 목적은 통계분석 기법과 머신러닝 기법을 활용한 공간분석을 통해 건물의 지진취약도를 비교분석 하는 것이다. 통계분석 기법을 활용한 결과, 최적화척도법을 활용해 개발된 모델의 예측정확도는 약 87%로 도출되었다. 머신러닝 기법을 활용한 결과, 분석된 4가지 방법 중, Random Forest의 정확도가 Train Set의 경우 94%, Test Set의 경우 76.7%로 가장 높아, 최종적으로 Random Forest가 선정되었다. 따라서, 예측정확도는 통계분석 기법이 약 87%, 머신러닝 기법이 76.7%로, 통계분석 기법의 예측정확도가 더 높은 것으로 분석되었다. 최종 결과로, 건물의 지진취약도는 분석된 건물데이터 총 22,296개 중, 1,627(0.1%)개의 건물데이터는 통계분석 기법 사용 시 더 위험하다고 도출되었고, 10,146(49%)개의 건물데이터는 동일하게 도출되었으며, 나머지 10,523(50%)개의 건물데이터는 머신러닝 기법 사용 시 더 위험하게 도출되었다. 기존 통계분석 기법에 첨단 머신러닝 기법활용결과가 추가로 비교검토 됨으로써 공간분석 의사결정에 있어서, 좀더 신뢰도가 높은 지진대응책 마련에 도움이 되길 기대한다.