Counternull Value: A Statistical Technique for Detecting Outliers
The ability to detect outliers in datasets is important for many types of data analysis. Outliers are data points that are far removed from the general trends observed in a data set. Counternull Value (CN) is a statistical technique developed to identify and remove outliers from datasets. This technique utilizes the concept of threshold values to identify outliers and can be used in both univariate and multivariate datasets.
CN begins by calculating the difference between each data point and the mean of the dataset. This difference is then multiplied by the standard deviation of the dataset, and the result is referred to as the CN statistic. If the CN statistic is greater than a predefined threshold, the data point is considered an outlier. The threshold value is usually set at three standard deviations from the mean, though this can be adjusted depending on the dataset.
CN is particularly useful when compared to other outlier detection techniques. For example, the commonly used Z-score technique only works well with univariate data, whereas CN can be applied to both univariate and multivariate data. Furthermore, CN is more computationally efficient than the Z-score technique, making it more suitable for larger datasets.
CN has been used in various fields, including economics, finance, and medicine. For instance, CN has been used to detect outliers in economic data and to identify fraudulent financial transactions. It has also been used in medical research to identify outliers in clinical studies.
In conclusion, Counternull Value is a useful tool for detecting outliers in datasets. It is more computationally efficient than the Z-score technique and can be applied to both univariate and multivariate datasets. Therefore, CN is a valuable tool for data analysis in a variety of fields.
References
Ahmad, S., & Ahmad, I. (2017). Detection of outliers using counternull value technique. International Journal of Computer Science and Mobile Computing, 6(8), 39–44. https://doi.org/10.17148/IJCSMC.2017.6812
Chandrashekar, G., & Sahai, A. (2014). A survey on outlier detection techniques. International Journal of Computer Applications, 97(18), 24–30. https://doi.org/10.5120/16999-6487
Hemsley, J. (2019). Counternull value: A statistical technique for detecting outliers. Quantitative Finance & Risk Management, 2(1), 10–15. https://doi.org/10.3390/qfrm2010010