Browse > Article
http://dx.doi.org/10.7465/jkdi.2012.23.4.657

Outlier detection using Grubb test and Cochran test in clinical data  

Sohn, Ki-Cheul (School of Medicine, Catholic University of Daegu)
Shin, Im-Hee (School of Medicine, Catholic University of Daegu)
Publication Information
Journal of the Korean Data and Information Science Society / v.23, no.4, 2012 , pp. 657-663 More about this Journal
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.
Keywords
Clinical data; Cochran-test; Grubb-test; outlier;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Ahn, B. J. and Seo, H. S. (2011). Outlier detection using dynamic plots. The Korean Journal of Applied Statistics, 24, 979-986.   DOI   ScienceOn
2 Barret, V. and Lewis, T. (1994). Outliers in statistical data, 3rd Edition, John Wiley, England.
3 Burke, S. (2001). Missing values, outliers, robust statistics and non-parametric methods. Statistics and Data Analysis, LC.GC Europe online Supplement, 19-24.
4 Gentleman, J. F. and Wilk, M. B. (1975). Detecting outliers II: Supplementing the direct analysis of residuals. Biometrics, 31, 387-410.   DOI   ScienceOn
5 Little, R. J. A. and Rubin, D. B. (1987). Statistical analysis with missing data, John Wiley & Sons, United States of America.
6 Seo, H. S. and Yoon, M. (2011). Outlier detection using support vertor machines. Communications of the Korean Statistical Society, 18, 171-177.   DOI   ScienceOn
7 Song, G. M., Moon, J. E. and Park, C. (2011). Realization of an outlier detection algorithm using R. Journal of the Korean Data & Information Science Society, 22, 449-458.
8 Ulleberg, T., Robben, J., Nordahl, K. M., Ulleberg, T. and Heiene, R. (2011). Plasma creatinine in dogs: Intra and inter laboratory variation in 10 European veterinary laboratories. Acta Velerinaria Scandinavica, 53, 1-13.   DOI   ScienceOn
9 Walfish, S. (2006). A review of statistical outlier methods. Pharmaceutical Technology, 2, 1-5.