A NEW HYBRID METHOD FOR DATA ANALYSIS WHEN A SIGNIFICANT PERCENTAGE OF DATA IS MISSING.

المقال يناقش طريقة هجينة جديدة لتحليل البيانات تعالج مشكلة البيانات المفقودة من خلال مقارنة تقنيات الإحلال المختلفة. يقوم بتقييم الطرق التقليدية مثل المتوسط، الوسيط، التقدير المتوقع-التعظيم (EM)، الإحلال بالانحدار (RI)، والإحلالات المتعددة (MI)، إلى جانب ثلاثة طرق مركبة مقترحة: EM مع MI (EMMI)، EM مع RI (EMR)، وRI مع MI (RMI). تستخدم الدراسة مجموعة بيانات من تجارة الحاويات المائية بواسطة مصلحة الجمارك الأمريكية (2000-2017) لتقييم كفاءة هذه الطرق بناءً على معايير مثل متوسط الخطأ التربيعي (MSE) والانحراف المعياري (SD). تشير النتائج إلى أن الطرق المركبة تتفوق عمومًا على الطرق التقليدية، خاصة في السيناريوهات التي تحتوي على نسب عالية من البيانات المفقودة. [Extracted from the article]

This article aims to compare the effciency of different imputation methods with missing data. We use mean, median, Expected-Maximization (EM), regression imputation(RI) and multiple imputations (MI) to replace missing data. In fact, we employ three proposed combination methods, namely EM imputation with MI imputation (EMMI), EM imputation with regression imputation (EMR), and regression imputation with MI imputation (MI). We will compare these methods using an example study of Waterborne Container Trade by the US Customs Port (2000-2017) where the methods with different missing percentages. Several criteria, are used to compare estimations effciency, such as mean, Standard Deviation (SD), and Mean Squared Error (MSE). The results show that the eficiency of composite imputation methods in almost all situations, in terms of MSE, RMI imputation method outperforms other methods. Nevertheless, when the missing percentage is small, the EMR imputation method performs better. In terms of the SD criterion, we find that the MI method is better than the other methods, where the RMI method is good when the missing percentage is large. When the missing percentage is in the range (40-50%), the EMR and RMI imputation methods give a better MSE. [ABSTRACT FROM AUTHOR]

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