Outlier detection using Grubb test and Cochran test in clinical data

Sohn, Ki-Cheul; Shin, Im-Hee

doi:10.7465/jkdi.2012.23.4.657

Journal of the Korean Data and Information Science Society

2012

DOI: 10.7465/jkdi.2012.23.4.657

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Outlier detection using Grubb test and Cochran test in clinical data

Ki-Cheul Sohn¹,

Im-Hee Shin²

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2016

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Outlier detection and treatment in industrial sampling survey

Joo¹,

Cho²

2016

Journal of the Korean Data and Information Science Society

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Outliers in surveys can have a large effect on estimates of totals. This is especially true in business surveys where the populations are drawn are typically skewed. In this paper, we discussed the practical development and implementation of methods to identify and deal with outliers. A detection method is based on quartile method and detected outlier is processed in various ways. The study examines two versions of winsorised estimators with three different cut-off thresholds for each one. For the simulation study, four types of weight transformation function have been considered.

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Outlier detection and treatment in industrial sampling survey

Joo¹,

Cho²

2016

Journal of the Korean Data and Information Science Society

View full text Add to dashboard Cite

show abstract

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Outlier detection using Grubb test and Cochran test in clinical data

Cited by 1 publication

References 4 publications

Outlier detection and treatment in industrial sampling survey

Outlier detection and treatment in industrial sampling survey

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