Detecting Outliers in the Simple Linear Regression for Children Affected with Leukemia in Mosul City
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Abstract
In this study, the problem of outlier detection in “linear regression” analysis is studied using the “median” and “mean” “absolute deviation” about the median. “The mean and standard deviation” are heavily affected by outliers Hence, the outlier detection techniques based on these measures may not correctly identify all outliers in a dataset. However, “the mean absolute deviation about the median”, in combination with the median is sufficiently robust in the presence of outliers and provides a better alternative. The conceptualized method was tested using leukemia patients data and the results indicate that the new method performed better than the methods based on the mean/ standard deviation combination. It is recommended that the median and “mean absolute deviation” about the median be used in detecting outliers in regression analysis due to their inherent potential for increasing the” goodness-of-fit of the “linear regression mode
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References
[1] Ogu, A. I., Inyama, S. C., and Achugamonu, P. C. 2013. “Methods of detecting outliers in a regression analysis model”. West African Journal of Industrial and Academic Research, 7(1), 105-112.
[2] Dervillis, N., Worden, K. and Cross, E. J. 2015. “On robust regression analysis as a means of exploring environmental and operational conditions for SHM data”. Journal of Sound and Vibration, 347, 279-296.
[3] Cohen, A. R., Galanellor, R., Pennel, C. J., Cunningham, M. J., and Vichinsky, E. 2004. Leukemia. ASH Program Book (1), 14-34.
[4] Pham-Gia, T. and Hung, T. L. 2001. The mean and median absolute deviations. Mathematical and Computer Modelling, 34, 921-936.
[5] Gupta, S. C. 2011. Fundamentals of Statistics. Himalaya Publishing House. India.
Hameed, M., Raziq, F., and Mir, A. 2020.” Correlation of serum ferritin with hemoglobin A2 level in beta-leukemia traits”. Journal of Ayub Medical College, Abottabad, 32(4), 476-480.
[6] Laurikkala, J., Juhola, M., Kentala, E., Lavrac, N., Miksch, S. and Kavsek, B. 2000. “Informal identification of outliers in medical data”. Fifth International Workshop on Intelligent Data Analysis in Medicine and Pharmacology, 1, 20-24.
[7] Rousseeuw, P. and Hubert, M. 2011. “Robust statistics for outlier detection”. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1(1), 73-79.
[8] Hauskrecht, M., Batal, I., Valko, M., Visweswaran, S., Cooper, G. F. and Clermont, G. 2013. Outlier detection for patient monitoring and alerting. Journal of Biomedical Informatics, 46(1), 47-55.
[9] Christy, A., Gandhi, G. M. and Vaithyasubramanian, S. 2015.” Cluster based outlier detection algorithm for healthcare data”. Procedia Computer Science, 50, 209-215.
[10] Qi, M., Fu, Z., and Chen, F. 2015. “Outlier detection method of multiple measuring points of parameters in power plant units”. Applied Thermal Engineering, 85, 297-303.
[11] Bonnet, D. G. and Seier, E. 2003. Confidence Intervals for Mean Absolute Deviations. The American Statistician, 57(4), 233-236.
[12] Boos, D. D. and Brownie, C. 2004. “Comparing Variances and other Measures of Dispersion”. Statistical Science, 19(4), 571-578.
[13] Elamir, E. A. (2012). Mean absolute deviation about median as a tool for explanatory data analysis. Proceedings of the World Congress on Engineering, 1.
[14] Sanni, O. O. M., Ikoba, N. A., and Adegboye, O. S. 2021. Modified methods of computing some descriptive statistics for grouped data. Benin Journal of Statistics, 4, 113-133.
[15] Stephen, R. S. and Senthamarai, K. K. 2017. “Detection of outliers in regression model for medical data”. International Journal for Medical Research & Health Sciences, 6(7), 50-56.
[16] Danjou, F., Anni, F., Perseu, L., Satta, S., Dessi, C., Lai, M. E., Fortina, P., Devoto, M., and Glanello, R. 2012. “Genetic modifiers of β-leukemia and clinical severity as assessed by age at first transfusion”. Haematological, 97(7), 989.
[17] Nakavachara, P., Petchkul, J., Jeerawongpanich, K., Kiattissakthree, P., Manpayak, T., Netsakulnee, P., Chaichanwattanakul, K., Pooliam, J., Srichairatanakool, S., and Viprakasit, V. 2018. Prevalence of low bone mass among adolescents with non-transfusion-dependent hem1oglobin E/ β-leukemia and its relationship with anemia severity. Pediatric Blood & Cancer, 65(1), e26744.
[18] Bayati, S., Keikhaei, B., Bahadoram, M., Mahmoudian-Sani, M., Vanaeshani, M., and Behbahani, F. 2021. “Radiographic features of the maxillofacial anomalies in beta-leukemia major: with new view”. World Journal of Plastic Surgery, 10(3), 78.
[19] Bluman, A. G.2012. Elementary Statistics: A Step by Step Approach. McGraw-Hill, New York.
[20] Choulakian, V. and Abou-Samra, G. 2020. “Mean Absolute Deviations about the Mean, the Cut Norm and Taxicab Correspondence Analysis”. Open Journal of Statistics, 10, 97-112.