그럽 및 코크란 검정을 이용한 임상자료의 이상치 판단 = Outlier detection using Grubb test and Cochran test in clinical data

다국어 초록 (Multilingual Abstract)

There are very small values and/or very big values which get out of the normal range for survey data in various fields. The reasons of occurrence for outlier are two. One of them is the error in process of data input and the other is the strange response of the respondent. If the data has outliers, then the summary statistics such as the mean and the variance produce misleading information. Therefore, researcher should be careful in detecting the outlier in data. In particular, it is very important problem for clinical fields because the cost of experiment is very high. This article introduce the Grubb test and Cochran test to detect outliers in the data and we apply this method for clinical data.

참고문헌 (Reference)

1 서한손, "서포트 벡터 기계를 이용한 이상치 진단" 한국통계학회 18 (18): 171-177, 2011

2 안병진, "동적 그림을 이용한 이상치 검색" 한국통계학회 24 (24): 979-986, 2011

3 Little, R. J. A., "Statistical analysis with missing data" John Wiley & Sons 1987

4 송규문, "R을 이용한 이상점 탐지 알고리즘의 구현" 한국데이터정보과학회 22 (22): 449-458, 2011

5 Ulleberg, T., "Plasma creatinine in dogs: Intra and inter laboratory variation in 10 European veterinary laboratories" 53 : 1-13, 2011

6 Barret, V., "Outliers in statistical data, 3rd Edition" John Wiley 1994

7 Burke, S., "Missing values, outliers, robust statistics and non-parametric methods"

8 Gentleman, J. F., "Detecting outliers II: Supplementing the direct analysis of residuals" 31 : 387-410, 1975

9 Walfish, S., "A review of statistical outlier methods" 2 : 1-5, 2006